확률모델
•Download as PPT, PDF•
0 likes•249 views
This document discusses probabilistic models for information retrieval. It motivates using a probabilistic framework to model relevance as depending on query and document representations. The goal is to find an ideal answer set R that maximizes the probability of relevance to the user. Documents in R are predicted relevant, others non-relevant. Bayes' rule is used to define the probabilistic model, making independence assumptions. Initial probabilities are adjusted to address small values. The probabilistic model ranks documents by decreasing probability of relevance. A brief comparison is made of classic Boolean and vector space models.
Report
Share
Report
Share
![2.5.4 Probabilistic Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](https://image.slidesharecdn.com/random-091101190259-phpapp01/85/-1-320.jpg)
![Probabilistic Model ,[object Object],23 Using Bayes’ rule](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Probabilistic Retrieval Models - Sean Golliher Lecture 8 MSU CSCI 494
The document discusses probabilistic retrieval models in information retrieval. It provides an overview of older models like Boolean retrieval and vector space models. The main focus is on probabilistic models like BM25 and language models. It explains key concepts in probabilistic IR like the probability ranking principle, using Bayes' rule to estimate the probability that a document is relevant given features of the document, and estimating probabilities based on the frequencies of terms in relevant documents. The goal is to rank documents based on the probability of relevance to the query.
Interactive Latent Dirichlet Allocation
The document describes an interactive Latent Dirichlet Allocation (LDA) model that allows users to provide feedback to guide the topic modeling process. It summarizes previous work using constraints to encode feedback. It then introduces an approach using variational EM for LDA that allows modifying the topic distributions between epochs based on user feedback, such as removing words from topics, deleting topics, merging topics, or splitting topics. The interactive LDA approach alternates between running LDA to convergence and applying user updates to the topic distributions.
A Document Similarity Measurement without Dictionaries
The document proposes a measure of document similarity called Common Keyword Similarity (CKS) that does not rely on dictionaries. CKS is based on finding common substrings between documents using a PAT-tree data structure. The importance of each substring is determined by its discriminating effect (KDE), which reflects how well it fits a given classification system. CKS is computed as the sum of the weights of the common keywords between two documents. Experimental results on news articles show that CKS without a dictionary has better recall and precision than a method using cosine coefficient that relies on a dictionary, since many terms cannot be found in dictionaries. The classification system used to determine keyword weights also significantly impacts performance.
Author Topic Model
A detailed presentation of the paper 'The author-topic model for authors and documents' by M Rosen-Zvi
Artificial Intelligence
The document discusses various techniques for information retrieval and language modeling approaches to IR, including:
- Clustering documents into similar groups to aid in retrieval
- Using term frequency-inverse document frequency (TF-IDF) to measure word importance in documents
- Language models that represent documents and queries as probability distributions over words
- Smoothing language models to address data sparsity issues
- Cluster-based scoring methods that incorporate information from query-relevant document clusters
Boolean,vector space retrieval Models
The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Basic review on topic modeling
This document provides an introduction and overview of 5 papers related to topic modeling techniques. It begins with introducing the speaker and their research interests in text analysis using topic modeling. It then lists the 5 papers that will be discussed: LSA, pLSI, LDA, Gaussian LDA, and criticisms of topic modeling. The document focuses on summarizing each paper's motivation, key points, model, parameter estimation methods, and deficiencies. It provides high-level summaries of key aspects of influential topic modeling papers to introduce the topic.
A COMPARISON OF TEXT CATEGORIZATION METHODS
In this paper firstly I have compared Single Label Text Categorization with Multi Label Text Categorization in detail then I have compared Document Pivoted Categorization with Category Pivoted Categorization in detail. For this purpose I have given the general definition of Text Categorization with its mathematical notation for the purpose of its frugality and cost effectiveness. Then with the help of mathematical notation and set theory ,I have converted the general definitions of Single Label Text Categorization and Multi Label Text Categorization into their respective mathematical representation .Then I discussed Binary Text Categorization as a special case of Single Label Text Categorization. After comparison of Single Label Text Categorization with Multi Label Text Categorization, I found that Single Label Text Categorization or Binary Text Categorization is more general than Multi Label Text Categorization. Thereafter I discussed an algorithm for transformation of Multi Label Classification into Binary Classification and explained the conditions of transformation of Multi Label Classification into Binary Classification. In the second step I compared Document Pivoted Categorization with Category Pivoted Categorization in detail. After comparison we found that Category Pivoted Categorization is more typical and complex than Document Pivoted Categorization. The Category Pivoted Categorization becomes more complicated when new category is added to predefined set of categories and the recurrent classification of documents takes place. Finally I compared Hard Categorization with Ranking Categorization. After comparing them I found that Hard Categorization incorporates ‘Hard Decisions’ about the relevance or belonging of a document to a category. This hard decision is either completely true or completely false. Whereas the Ranking Categorization creates a belonging of a document to a category
according to the estimated appropriateness to the document. The final Ranked List is developed in the Ranking Categorization which is used by the human expert for final decision of Text Categorization.
Recommended
Probabilistic Retrieval Models - Sean Golliher Lecture 8 MSU CSCI 494
The document discusses probabilistic retrieval models in information retrieval. It provides an overview of older models like Boolean retrieval and vector space models. The main focus is on probabilistic models like BM25 and language models. It explains key concepts in probabilistic IR like the probability ranking principle, using Bayes' rule to estimate the probability that a document is relevant given features of the document, and estimating probabilities based on the frequencies of terms in relevant documents. The goal is to rank documents based on the probability of relevance to the query.
Interactive Latent Dirichlet Allocation
The document describes an interactive Latent Dirichlet Allocation (LDA) model that allows users to provide feedback to guide the topic modeling process. It summarizes previous work using constraints to encode feedback. It then introduces an approach using variational EM for LDA that allows modifying the topic distributions between epochs based on user feedback, such as removing words from topics, deleting topics, merging topics, or splitting topics. The interactive LDA approach alternates between running LDA to convergence and applying user updates to the topic distributions.
A Document Similarity Measurement without Dictionaries
The document proposes a measure of document similarity called Common Keyword Similarity (CKS) that does not rely on dictionaries. CKS is based on finding common substrings between documents using a PAT-tree data structure. The importance of each substring is determined by its discriminating effect (KDE), which reflects how well it fits a given classification system. CKS is computed as the sum of the weights of the common keywords between two documents. Experimental results on news articles show that CKS without a dictionary has better recall and precision than a method using cosine coefficient that relies on a dictionary, since many terms cannot be found in dictionaries. The classification system used to determine keyword weights also significantly impacts performance.
Author Topic Model
A detailed presentation of the paper 'The author-topic model for authors and documents' by M Rosen-Zvi
Artificial Intelligence
The document discusses various techniques for information retrieval and language modeling approaches to IR, including:
- Clustering documents into similar groups to aid in retrieval
- Using term frequency-inverse document frequency (TF-IDF) to measure word importance in documents
- Language models that represent documents and queries as probability distributions over words
- Smoothing language models to address data sparsity issues
- Cluster-based scoring methods that incorporate information from query-relevant document clusters
Boolean,vector space retrieval Models
The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Basic review on topic modeling
This document provides an introduction and overview of 5 papers related to topic modeling techniques. It begins with introducing the speaker and their research interests in text analysis using topic modeling. It then lists the 5 papers that will be discussed: LSA, pLSI, LDA, Gaussian LDA, and criticisms of topic modeling. The document focuses on summarizing each paper's motivation, key points, model, parameter estimation methods, and deficiencies. It provides high-level summaries of key aspects of influential topic modeling papers to introduce the topic.
A COMPARISON OF TEXT CATEGORIZATION METHODS
In this paper firstly I have compared Single Label Text Categorization with Multi Label Text Categorization in detail then I have compared Document Pivoted Categorization with Category Pivoted Categorization in detail. For this purpose I have given the general definition of Text Categorization with its mathematical notation for the purpose of its frugality and cost effectiveness. Then with the help of mathematical notation and set theory ,I have converted the general definitions of Single Label Text Categorization and Multi Label Text Categorization into their respective mathematical representation .Then I discussed Binary Text Categorization as a special case of Single Label Text Categorization. After comparison of Single Label Text Categorization with Multi Label Text Categorization, I found that Single Label Text Categorization or Binary Text Categorization is more general than Multi Label Text Categorization. Thereafter I discussed an algorithm for transformation of Multi Label Classification into Binary Classification and explained the conditions of transformation of Multi Label Classification into Binary Classification. In the second step I compared Document Pivoted Categorization with Category Pivoted Categorization in detail. After comparison we found that Category Pivoted Categorization is more typical and complex than Document Pivoted Categorization. The Category Pivoted Categorization becomes more complicated when new category is added to predefined set of categories and the recurrent classification of documents takes place. Finally I compared Hard Categorization with Ranking Categorization. After comparing them I found that Hard Categorization incorporates ‘Hard Decisions’ about the relevance or belonging of a document to a category. This hard decision is either completely true or completely false. Whereas the Ranking Categorization creates a belonging of a document to a category
according to the estimated appropriateness to the document. The final Ranked List is developed in the Ranking Categorization which is used by the human expert for final decision of Text Categorization.
Vsm 벡터공간모델
The document discusses different information retrieval models including boolean, vector, and probabilistic models. The boolean model uses set theory and boolean algebra to represent documents and queries. The vector model assigns weights to terms and ranks documents based on similarity to the query. The probabilistic model calculates the probability of a document being relevant given a query. It also covers structured models for different tasks like filtering and browsing.
Lifelong Topic Modelling presentation
Paper presentation for the final course Advanced Concept in Machine Learning.
The paper is @Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data"
http://jmlr.org/proceedings/papers/v32/chenf14.pdf
Topic model an introduction
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
TopicModels_BleiPaper_Summary.pptx
This document provides an overview of topic modeling. It defines topic modeling as discovering the thematic structure of a corpus by modeling relationships between words and documents through learned topics. The document introduces Latent Dirichlet Allocation (LDA) as a widely used topic modeling technique. It outlines LDA's generative process and inference methods like Gibbs sampling and variational inference. The document also discusses extensions to LDA, evaluation strategies, open questions, and applications like topic labeling and browsing.
Topic Models
Topic models are probabilistic models for discovering the underlying semantic structure of a document collection based on a hierarchical Bayesian analysis. Latent Dirichlet allocation (LDA) is a commonly used topic model that represents documents as mixtures of topics and topics as distributions over words. LDA uses Gibbs sampling to estimate the posterior distribution over topic assignments given the words in each document.
Probabilistic information retrieval models & systems
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Сергей Кольцов —НИУ ВШЭ —ICBDA 2015
1. The document proposes Granulated LDA (GLDA), a regularized version of LDA, to improve topic modeling stability.
2. It introduces measures like Kullback-Leibler divergence and Jaccard coefficient to evaluate topic similarity and modeling stability across runs.
3. An experiment applies LDA, SLDA, and GLDA to a large Russian text corpus, finding that GLDA produces more stable topics across multiple runs according to these measures.
Topics Modeling
This document summarizes the agenda and key topics for a CIS 890 project final presentation on topics modelling with LDA. The presentation will cover LDA modelling, HMMLDA modelling, LDA with collocations modelling, and experimental results on the NIPS collection. It will discuss topic modelling approaches like LDA, discriminative vs generative methods, and limitations of bag-of-words assumptions.
Topic Models - LDA and Correlated Topic Models
The document discusses topic models like Latent Dirichlet Allocation (LDA) and Correlated Topic Models (CTM). LDA is a generative probabilistic model that can discover topics in a collection of documents and represent documents as mixtures over latent topics. CTM extends LDA by allowing topic correlations, since LDA assumes topic independence. CTM models topic proportions using a logistic normal distribution rather than LDA's Dirichlet distribution, allowing dependencies between topics.
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey Gusev
Learning over images and understanding the quality of content play an important role at Pinterest. This talk will present a Spark based system responsible for detecting near (and far) duplicate images. The system is used to improve the accuracy of recommendations and search results across a number of production surfaces at Pinterest.
At the core of the pipeline is a Spark implementation of batch LSH (locality sensitive hashing) search capable of comparing billions of items on a daily basis. This implementation replaced an older (MR/Solr/OpenCV) system, increasing throughput by 13x and decreasing runtime by 8x. A generalized Spark Batch LSH is now used outside of the image similarity context by a number of consumers. Inverted index compression using variable byte encoding, dictionary encoding, and primitives packing are some examples of what allows this implementation to scale. The second part of this talk will detail training and integration of a Tensorflow neural net with Spark, used in the candidate selection step of the system. By directly leveraging vectorization in a Spark context we can reduce the latency of the predictions and increase the throughput.
Overall, this talk will cover a scalable Spark image processing and prediction pipeline.
Introduction to Probabilistic Latent Semantic Analysis
The document provides an introduction to Probabilistic Latent Semantic Analysis (PLSA). It discusses how PLSA improves on previous Latent Semantic Analysis methods by incorporating a probabilistic framework. PLSA models documents as mixtures of topics and allows words to have multiple meanings. The parameters of the PLSA model, including the topic distributions and word-topic distributions, are estimated using an expectation-maximization algorithm to find the parameters that best explain the observed word-document co-occurrence data.
Automated building of taxonomies for search engines
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
Supplementary material for my following paper: Infinite Latent Process Decomp...
This document proposes an infinite latent process decomposition (iLPD) model for microarray data. iLPD extends latent process decomposition (LPD) to allow for an infinite number of latent processes. It presents iLPD's generative process and joint distribution. The document also introduces auxiliary variables and provides a collapsed variational Bayesian inference approach for iLPD. This involves deriving a lower bound for the log evidence to evaluate iLPD's efficiency and compare it to LPD. The proposed method improves on past work by treating full posterior distributions over hyperparameters and removing dependencies in computing the variational lower bound.
Navigating and Exploring RDF Data using Formal Concept Analysis
In this study we propose a new approach based on Pattern Structures, an extension of Formal Concept Analysis, to provide exploration over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space provides interactive exploration over RDF data and allows user to visualize only the part of data that is interesting for her.
Probabilistic Retrieval
This document discusses incorporating probabilistic retrieval knowledge into TFIDF-based search engines. It provides an overview of different retrieval models such as Boolean, vector space, probabilistic, and language models. It then describes using a probabilistic model that estimates the probability of a document being relevant or non-relevant given its terms. This model can be combined with the BM25 ranking algorithm. The document proposes applying probabilistic knowledge to different document fields during ranking to improve relevance.
Spatial Approximate String Keyword content Query processing
Web users and content are increasingly being geo-positioned, and increased focus is being given
to serving local content in response to web queries. This development calls for spatial keyword queries that take
into account both the locations and textual descriptions of content. We study the efficient, joint processing of
multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also
occurs when multiple queries are used to obfuscate a user’s true query. To propose a novel algorithm and index
structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed
solution is efficient on real datasets. They also offer analytical studies on synthetic datasets to demonstrate the
efficiency of the proposed solution.
Latent dirichletallocation presentation
Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by probability distributions over words. LDA addresses limitations of previous topic models like pLSI by treating topic mixtures as random variables rather than document-specific parameters. Variational inference and EM algorithms are used for parameter estimation in LDA. Empirical results show LDA outperforms other models on tasks like document modeling, classification, and collaborative filtering.
Programs & Initiatives
Phi Theta Kappa is an international honor society that recognizes academic achievement in two-year colleges. Its purpose is to promote scholarship, leadership, service, and fellowship among its members. The document outlines Phi Theta Kappa's programs and initiatives, including civic engagement programs, recognitions programs, and programs focused on developing chapters through its Five Star Chapter Development Program. Chapters can earn stars by participating in leadership, service, fellowship and scholarship programs, with a focus on recognizing members and encouraging academic excellence.
Using Technology To Strengthen Chapter Communication
This document discusses ways that Phi Theta Kappa chapters can strengthen communication both internally and externally using technology. It provides examples of online tools and resources chapters can use to communicate important information to members through newsletters, websites, social media, and email. These include maintaining chapter directories and profiles on websites like MySpace and Facebook, creating chapter email accounts, and sharing newsletters, calendars, and announcements through emails, blogs, and discussion boards. Regional coordinators are also highlighted as important contacts for communicating between chapters and headquarters.
집합모델 확장불린모델
The document describes several alternative models for information retrieval, including fuzzy set models, extended Boolean models, generalized vector space models, latent semantic indexing models, neural network models, and Bayesian network models. It provides details on fuzzy set models that allow gradual membership in sets, extended Boolean models that combine Boolean queries with vector space characteristics, and Bayesian networks that use directed acyclic graphs and conditional probabilities.
Working for Justice in Australia
In 2001, there were approximately 99,900 homeless people in Australia. 58% of homeless people were alone, while 23% were families. Only 14,251 homeless individuals were staying in government-funded services. The government and charitable organizations are working to address homelessness by operating homeless shelters. The Catholic Church joins efforts by providing aid and housing assistance. Schools like Holy Spirit College help by collecting clothes and food for struggling families.
More Related Content
What's hot
Vsm 벡터공간모델
The document discusses different information retrieval models including boolean, vector, and probabilistic models. The boolean model uses set theory and boolean algebra to represent documents and queries. The vector model assigns weights to terms and ranks documents based on similarity to the query. The probabilistic model calculates the probability of a document being relevant given a query. It also covers structured models for different tasks like filtering and browsing.
Lifelong Topic Modelling presentation
Paper presentation for the final course Advanced Concept in Machine Learning.
The paper is @Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data"
http://jmlr.org/proceedings/papers/v32/chenf14.pdf
Topic model an introduction
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
TopicModels_BleiPaper_Summary.pptx
This document provides an overview of topic modeling. It defines topic modeling as discovering the thematic structure of a corpus by modeling relationships between words and documents through learned topics. The document introduces Latent Dirichlet Allocation (LDA) as a widely used topic modeling technique. It outlines LDA's generative process and inference methods like Gibbs sampling and variational inference. The document also discusses extensions to LDA, evaluation strategies, open questions, and applications like topic labeling and browsing.
Topic Models
Topic models are probabilistic models for discovering the underlying semantic structure of a document collection based on a hierarchical Bayesian analysis. Latent Dirichlet allocation (LDA) is a commonly used topic model that represents documents as mixtures of topics and topics as distributions over words. LDA uses Gibbs sampling to estimate the posterior distribution over topic assignments given the words in each document.
Probabilistic information retrieval models & systems
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Сергей Кольцов —НИУ ВШЭ —ICBDA 2015
1. The document proposes Granulated LDA (GLDA), a regularized version of LDA, to improve topic modeling stability.
2. It introduces measures like Kullback-Leibler divergence and Jaccard coefficient to evaluate topic similarity and modeling stability across runs.
3. An experiment applies LDA, SLDA, and GLDA to a large Russian text corpus, finding that GLDA produces more stable topics across multiple runs according to these measures.
Topics Modeling
This document summarizes the agenda and key topics for a CIS 890 project final presentation on topics modelling with LDA. The presentation will cover LDA modelling, HMMLDA modelling, LDA with collocations modelling, and experimental results on the NIPS collection. It will discuss topic modelling approaches like LDA, discriminative vs generative methods, and limitations of bag-of-words assumptions.
Topic Models - LDA and Correlated Topic Models
The document discusses topic models like Latent Dirichlet Allocation (LDA) and Correlated Topic Models (CTM). LDA is a generative probabilistic model that can discover topics in a collection of documents and represent documents as mixtures over latent topics. CTM extends LDA by allowing topic correlations, since LDA assumes topic independence. CTM models topic proportions using a logistic normal distribution rather than LDA's Dirichlet distribution, allowing dependencies between topics.
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey Gusev
Learning over images and understanding the quality of content play an important role at Pinterest. This talk will present a Spark based system responsible for detecting near (and far) duplicate images. The system is used to improve the accuracy of recommendations and search results across a number of production surfaces at Pinterest.
At the core of the pipeline is a Spark implementation of batch LSH (locality sensitive hashing) search capable of comparing billions of items on a daily basis. This implementation replaced an older (MR/Solr/OpenCV) system, increasing throughput by 13x and decreasing runtime by 8x. A generalized Spark Batch LSH is now used outside of the image similarity context by a number of consumers. Inverted index compression using variable byte encoding, dictionary encoding, and primitives packing are some examples of what allows this implementation to scale. The second part of this talk will detail training and integration of a Tensorflow neural net with Spark, used in the candidate selection step of the system. By directly leveraging vectorization in a Spark context we can reduce the latency of the predictions and increase the throughput.
Overall, this talk will cover a scalable Spark image processing and prediction pipeline.
Introduction to Probabilistic Latent Semantic Analysis
The document provides an introduction to Probabilistic Latent Semantic Analysis (PLSA). It discusses how PLSA improves on previous Latent Semantic Analysis methods by incorporating a probabilistic framework. PLSA models documents as mixtures of topics and allows words to have multiple meanings. The parameters of the PLSA model, including the topic distributions and word-topic distributions, are estimated using an expectation-maximization algorithm to find the parameters that best explain the observed word-document co-occurrence data.
Automated building of taxonomies for search engines
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
Supplementary material for my following paper: Infinite Latent Process Decomp...
This document proposes an infinite latent process decomposition (iLPD) model for microarray data. iLPD extends latent process decomposition (LPD) to allow for an infinite number of latent processes. It presents iLPD's generative process and joint distribution. The document also introduces auxiliary variables and provides a collapsed variational Bayesian inference approach for iLPD. This involves deriving a lower bound for the log evidence to evaluate iLPD's efficiency and compare it to LPD. The proposed method improves on past work by treating full posterior distributions over hyperparameters and removing dependencies in computing the variational lower bound.
Navigating and Exploring RDF Data using Formal Concept Analysis
In this study we propose a new approach based on Pattern Structures, an extension of Formal Concept Analysis, to provide exploration over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space provides interactive exploration over RDF data and allows user to visualize only the part of data that is interesting for her.
Probabilistic Retrieval
This document discusses incorporating probabilistic retrieval knowledge into TFIDF-based search engines. It provides an overview of different retrieval models such as Boolean, vector space, probabilistic, and language models. It then describes using a probabilistic model that estimates the probability of a document being relevant or non-relevant given its terms. This model can be combined with the BM25 ranking algorithm. The document proposes applying probabilistic knowledge to different document fields during ranking to improve relevance.
Spatial Approximate String Keyword content Query processing
Web users and content are increasingly being geo-positioned, and increased focus is being given
to serving local content in response to web queries. This development calls for spatial keyword queries that take
into account both the locations and textual descriptions of content. We study the efficient, joint processing of
multiple top-k spatial keyword queries. Such joint processing is attractive during high query loads and also
occurs when multiple queries are used to obfuscate a user’s true query. To propose a novel algorithm and index
structure for the joint processing of top-k spatial keyword queries. Empirical studies show that the proposed
solution is efficient on real datasets. They also offer analytical studies on synthetic datasets to demonstrate the
efficiency of the proposed solution.
Latent dirichletallocation presentation
Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by probability distributions over words. LDA addresses limitations of previous topic models like pLSI by treating topic mixtures as random variables rather than document-specific parameters. Variational inference and EM algorithms are used for parameter estimation in LDA. Empirical results show LDA outperforms other models on tasks like document modeling, classification, and collaborative filtering.
What's hot (18)
Probabilistic information retrieval models & systems
Probabilistic information retrieval models & systems
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey Gusev
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey Gusev
Introduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic Analysis
Automated building of taxonomies for search engines
Automated building of taxonomies for search engines
Supplementary material for my following paper: Infinite Latent Process Decomp...
Supplementary material for my following paper: Infinite Latent Process Decomp...
Navigating and Exploring RDF Data using Formal Concept Analysis
Navigating and Exploring RDF Data using Formal Concept Analysis
Spatial Approximate String Keyword content Query processing
Spatial Approximate String Keyword content Query processing
Viewers also liked
Programs & Initiatives
Phi Theta Kappa is an international honor society that recognizes academic achievement in two-year colleges. Its purpose is to promote scholarship, leadership, service, and fellowship among its members. The document outlines Phi Theta Kappa's programs and initiatives, including civic engagement programs, recognitions programs, and programs focused on developing chapters through its Five Star Chapter Development Program. Chapters can earn stars by participating in leadership, service, fellowship and scholarship programs, with a focus on recognizing members and encouraging academic excellence.
Using Technology To Strengthen Chapter Communication
This document discusses ways that Phi Theta Kappa chapters can strengthen communication both internally and externally using technology. It provides examples of online tools and resources chapters can use to communicate important information to members through newsletters, websites, social media, and email. These include maintaining chapter directories and profiles on websites like MySpace and Facebook, creating chapter email accounts, and sharing newsletters, calendars, and announcements through emails, blogs, and discussion boards. Regional coordinators are also highlighted as important contacts for communicating between chapters and headquarters.
집합모델 확장불린모델
The document describes several alternative models for information retrieval, including fuzzy set models, extended Boolean models, generalized vector space models, latent semantic indexing models, neural network models, and Bayesian network models. It provides details on fuzzy set models that allow gradual membership in sets, extended Boolean models that combine Boolean queries with vector space characteristics, and Bayesian networks that use directed acyclic graphs and conditional probabilities.
Working for Justice in Australia
In 2001, there were approximately 99,900 homeless people in Australia. 58% of homeless people were alone, while 23% were families. Only 14,251 homeless individuals were staying in government-funded services. The government and charitable organizations are working to address homelessness by operating homeless shelters. The Catholic Church joins efforts by providing aid and housing assistance. Schools like Holy Spirit College help by collecting clothes and food for struggling families.
Effective Membership Campaigns
The document provides tips and strategies for effective Phi Theta Kappa membership campaigns. It recommends identifying eligible students, sending invitation letters, holding orientations, and making follow-up phone calls to promote membership. Sample orientation agendas, recruitment videos, posters, and other resources are available online. Chapters are encouraged to get faculty and administrators involved, lower fees if possible, and promote the $36 million in transfer scholarships available to members. Increasing visibility by having officers wear badges and shirts during the campaign period can also help boost new member acceptance.
How to Become a Thought Leader in Your Niche
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Viewers also liked (6)
Using Technology To Strengthen Chapter Communication
Using Technology To Strengthen Chapter Communication
Similar to 확률모델
Search Engines
This document provides an overview of various information retrieval models and techniques used in search engines, including:
- Boolean, vector space, probabilistic models like BM25, and language models are described as older retrieval models.
- Learning to rank uses machine learning techniques to optimize ranking functions using features and training data.
- Relevance feedback, query likelihood models, and pseudo-relevance feedback are discussed as techniques for improving retrieval effectiveness by incorporating user feedback.
집합모델 확장불린모델
The document describes several alternative models for information retrieval, including fuzzy set models, extended Boolean models, generalized vector space models, latent semantic indexing models, neural network models, and Bayesian network models. It provides details on fuzzy set models that allow gradual membership in sets, extended Boolean models that combine Boolean queries with vector space characteristics, and Bayesian networks that use directed acyclic graphs and conditional probabilities.
Vsm 벡터공간모델
The document discusses different information retrieval models including boolean, vector, and probabilistic models. The boolean model uses set theory and boolean algebra to represent documents and queries. The vector model assigns non-binary weights to terms and ranks documents by similarity to the query. The probabilistic model assigns a probability of relevance to each document given a query.
4-IR Models_new.ppt
The document discusses the vector space model used in information retrieval. It explains that documents and queries are represented as weighted vectors in a multidimensional space. Similar vectors are close to each other. The weights used are usually tf-idf, which considers both the frequency of a term within a document and its rarity across documents. Documents are ranked based on the similarity between their vector representation and the query vector.
4-IR Models_new.ppt
The document discusses the vector space model used in information retrieval. It explains that documents and queries are represented as weighted vectors in a high dimensional vector space. Similarities between queries and documents are calculated to rank documents by relevance. Weights are often calculated using TF-IDF, which considers the frequency of terms within documents and across collections. Documents with vector representations closer to the query vector are considered more relevant.
Chapter 4 IR Models.pdf
The document discusses information retrieval (IR) models, including the Boolean, vector space, and probabilistic models. The Boolean model represents documents and queries as sets of index terms and determines relevance through binary term presence, while the vector space model represents documents and queries as weighted vectors in a multidimensional space and ranks documents by calculating similarity between document and query vectors. The probabilistic model determines relevance probabilities based on the likelihood of terms appearing in relevant vs. non-relevant documents.
Document ranking using qprp with concept of multi dimensional subspace
This presentation discusses a project titled "Document Ranking Using QPRP with Concept of Multi-Dimensional Subspace". It was presented by Prakash Kumar Dubey and guided by Mr. Sourish Dhar and Mr. Bhagaban Swain of the Department of IT. The presentation provides an overview of the project, including an introduction to information retrieval, classical IR models such as Boolean, vector space, and probabilistic models. It then discusses quantum probability and how it can be applied to document ranking. The presentation outlines the proposed solution, data collection and implementation, and concludes with future work.
Knowledge Discovery Query Language (KDQL)
The document discusses Knowledge Discovery Query Language (KDQL), a proposed query language for interacting with i-extended databases in the knowledge discovery process. KDQL is designed to handle data mining rules and retrieve association rules from i-extended databases. The key points are:
1) KDQL is based on SQL and is intended to support tasks like association rule mining within the ODBC_KDD(2) model for knowledge discovery.
2) It can be used to query i-extended databases, which contain both data and discovered patterns.
3) The KDQL RULES operator allows users to specify data mining tasks like finding association rules that satisfy certain frequency and confidence thresholds.
Cluster
The document discusses various techniques for document clustering and choosing the number of clusters. It describes how clustering can help address issues like ambiguous queries by grouping query results by topic. It also covers partitioning and hierarchical clustering approaches, as well as methods for choosing the number of clusters like hypothesis testing, Bayesian estimation, and penalizing model complexity. Visualization techniques like multidimensional scaling and self-organizing maps are also summarized.
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
The document presents four probabilistic models for faceted topic retrieval: MMR, a probabilistic interpretation of MMR, greedy result set pruning, and a probabilistic set-based approach. It describes an experiment comparing these four models on a test collection with 60 queries and human-annotated facets. The probabilistic set-based approach, which estimates facet models and document-facet probabilities, outperformed the other three models in terms of S-recall and redundancy based on a five-fold cross-validation experiment.
Probabilistic retrieval model
The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Topic Extraction on Domain Ontology
This document discusses topic extraction for domain ontology. It describes domain ontology as a collection of vocabularies and conceptualization of a given domain. The purpose of topic extraction is to identify relevant concepts in documents, obtain domain-specific terms, classify documents, and identify key concepts and relationships for an ontology. The project stages include obtaining domain knowledge, preprocessing documents, and applying either K-Means clustering or Latent Dirichlet Allocation to extract topics. K-Means partitions data into clusters while LDA represents documents as mixtures over topics characterized by word distributions.
Dimensionality Reduction Techniques for Document Clustering- A Survey
Abstract— Dimensionality reduction technique is applied to get rid of the inessential terms like redundant and noisy terms in documents. In this paper a systematic study is conducted for seven dimensionality reduction methods such as Latent Semantic Indexing (LSI), Random Projection (RP), Principle Component Analysis (PCA) and CUR decomposition, Latent Dirichlet Allocation(LDA), Singular value decomposition (SVD). Linear Discriminant Analysis(LDA)
Context-dependent Token-wise Variational Autoencoder for Topic Modeling
This document proposes a new variational autoencoder (VAE) approach for topic modeling that addresses the issue of latent variable collapse. The proposed VAE models each word token separately using a context-dependent sampling approach. It minimizes a KL divergence term not considered in previous VAEs for topic modeling. An experiment on four large datasets found the proposed VAE improved over existing VAEs for about half the datasets in terms of perplexity or normalized pairwise mutual information.
Probabilistic Topic models
This document provides an overview of probabilistic topic models. It discusses latent Dirichlet allocation (LDA), a commonly used topic model, and how it represents documents as mixtures over latent topics and words as generated by those topics. Parameter selection and inference algorithms for LDA are also summarized. Evaluation methods for topic models like held-out likelihood, topic coherence, and topic intrusion are outlined to assess how well models fit data and how interpretable topics are for humans.
Text Similarities - PG Pushpin
The document discusses text similarity techniques for detecting duplicate or plagiarized documents. It covers topics like data retrieval methods including TF-IDF, document term matrices, vector space models and latent semantic analysis. For similarity measurements, it describes cosine similarity and second order co-occurrence pointwise mutual information. It also discusses applications in plagiarism detection and copyright violation. Finally, it outlines a prototype for calculating text similarity between files using TF-IDF and measures like cosine, Pearson and co-occurrence similarities.
Semantic Annotation of Documents
The document describes a project to semantically annotate research papers with ACM classification categories. It discusses using cosine similarity, latent Dirichlet allocation, and a proposed model combining labeled LDA and doc2vec. The proposed model trains a supervised topic model to learn document representations that capture semantic relationships between papers and categories. The model achieved 59.31% mean average precision and 45.03% NDCG on a test dataset, demonstrating an improvement over baselines.
A scalable gibbs sampler for probabilistic entity linking
This document summarizes a research paper that proposes a scalable Gibbs sampling approach for probabilistic entity linking. The approach formulates entity linking as probabilistic inference in a topic model where each topic corresponds to a Wikipedia article. It introduces an efficient Gibbs sampling scheme that exploits the sparsity in the Wikipedia-LDA model to allow inference over millions of topics. Experimental results show it achieves state-of-the-art performance on the Aida-CoNLL dataset.
Lecture 9 - Machine Learning and Support Vector Machines (SVM)
This document discusses machine learning and support vector machines. It provides examples of using probabilities to determine the likelihood of a document being relevant given certain terms. It also discusses language models and smoothing techniques used in document ranking. Finally, it briefly outlines different types of machine learning problems and algorithms like supervised learning, classification, and reinforcement learning.
Slides
This document summarizes key concepts in information retrieval systems and algorithms for large data sets. It discusses the differences between information retrieval and data retrieval systems. It also describes several classic models for relevance ranking in IR, including the Boolean model and vector space model. The document outlines topics like text processing, indexing, searching, and evaluation in information retrieval systems.
Similar to 확률모델 (20)
Document ranking using qprp with concept of multi dimensional subspace
Document ranking using qprp with concept of multi dimensional subspace
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Probabilistic Models of Novel Document Rankings for Faceted Topic Retrieval
Dimensionality Reduction Techniques for Document Clustering- A Survey
Dimensionality Reduction Techniques for Document Clustering- A Survey
Context-dependent Token-wise Variational Autoencoder for Topic Modeling
Context-dependent Token-wise Variational Autoencoder for Topic Modeling
A scalable gibbs sampler for probabilistic entity linking
A scalable gibbs sampler for probabilistic entity linking
Lecture 9 - Machine Learning and Support Vector Machines (SVM)
Lecture 9 - Machine Learning and Support Vector Machines (SVM)
Recently uploaded
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Recently uploaded (20)
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf