SlideShare a Scribd company logo

PPT slides

•Download as PPT, PDF•
•
B
butest
1 of 23
Relational Learning with Gaussian Processes By Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S.Sathiya Keerthi (Columbia, Chicago, Cambridge, Yahoo!) Presented by Nesreen Ahmed, Nguyen Cao, Sebastian Moreno, Philip Schatz
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
[object Object],Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 where
[object Object],[object Object],Relational Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 x j x i ε ij
[object Object],Relational Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 i,j runs over the set of observed undirected linkages EP algorithm approximates where as : is a 2x2 symmetric matrix
Relational Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 where is a nxn matrix with four non-zero entries augmented from
[object Object],Relational Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 where elements of covariance matrix are given by evaluating the following (covariance) kernel function:
Linkage Prediction ,[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1
Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1 Nearest Neighborhood K=1
Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1 Nearest Neighborhood K=2
Semi supervised learning ,[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Semi supervised learning ,[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
12/02/08 CS590M: Statistical Machine Learning - Fall 2008 30 Samples collected from a gaussian mixture with two components on the x-axis. Two labeled samples indicated by diamond and circle. K=3 Best value =0.4 based on approximate model evidence Results
12/02/08 CS590M: Statistical Machine Learning - Fall 2008 Posterior Covariance matrix of RGP learnt from the data It captures the density information of unlabelled data Using the posterior covariance matrix learnt from the data as the new prior, supervised learning is carried out Curves represent predictive distribution for each class Results
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008 Student or Not Other or Not Univ. GPC LapSVM RGP GPC LapSVM RGP Corn. 0.825±0.016 0.987±0.008 0.989±0.009 0.708±0.021 0.865±0.038 0.884±0.025 Texa. 0.899±0.016 0.994±0.007 0.999±0.001 0.799±0.021 0.932±0.026 0.906±0.026 Wash. 0.839±0.018 0.957±0.014 0.961±0.009 0.782±0.023 0.828±0.025 0.877±0.024 Wisc 0.883±0.013 0.976±0.029 0.992±0.008 0.839±0.014 0.812±0.030 0.899±0.015
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],12/02/08 CS590M: Statistical Machine Learning - Fall 2008
12/02/08 CS590M: Statistical Machine Learning - Fall 2008 Thanks Questions ?

Recommended

Quantile Quantile Plot qq plot
Quantile Quantile Plot qq plot Quantile Quantile Plot qq plot
Quantile Quantile Plot qq plot Saeed Siddik
 
Real Time Geodemographics
Real Time GeodemographicsReal Time Geodemographics
Real Time GeodemographicsDr Muhammad Adnan
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsAlbert Bifet
 
Toward the Online Visualisation of Algorithm Performance for Parameter Selection
Toward the Online Visualisation of Algorithm Performance for Parameter SelectionToward the Online Visualisation of Algorithm Performance for Parameter Selection
Toward the Online Visualisation of Algorithm Performance for Parameter Selectiondjw213
 
Tetiana Bogodorova "Data Science for Electric Power Systems"
Tetiana Bogodorova "Data Science for Electric Power Systems"Tetiana Bogodorova "Data Science for Electric Power Systems"
Tetiana Bogodorova "Data Science for Electric Power Systems"Lviv Startup Club
 
Probabilistic data structures
Probabilistic data structuresProbabilistic data structures
Probabilistic data structuresYoav chernobroda
 
Sentiment Knowledge Discovery in Twitter Streaming Data
Sentiment Knowledge Discovery in Twitter Streaming DataSentiment Knowledge Discovery in Twitter Streaming Data
Sentiment Knowledge Discovery in Twitter Streaming DataAlbert Bifet
 
Pitfalls in benchmarking data stream classification and how to avoid them
Pitfalls in benchmarking data stream classification and how to avoid themPitfalls in benchmarking data stream classification and how to avoid them
Pitfalls in benchmarking data stream classification and how to avoid themAlbert Bifet
 

Recommended

Quantile Quantile Plot qq plot
Quantile Quantile Plot qq plot Quantile Quantile Plot qq plot
Quantile Quantile Plot qq plot Saeed Siddik
 
Real Time Geodemographics
Real Time GeodemographicsReal Time Geodemographics
Real Time GeodemographicsDr Muhammad Adnan
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsAlbert Bifet
 
Toward the Online Visualisation of Algorithm Performance for Parameter Selection
Toward the Online Visualisation of Algorithm Performance for Parameter SelectionToward the Online Visualisation of Algorithm Performance for Parameter Selection
Toward the Online Visualisation of Algorithm Performance for Parameter Selectiondjw213
 
Tetiana Bogodorova "Data Science for Electric Power Systems"
Tetiana Bogodorova "Data Science for Electric Power Systems"Tetiana Bogodorova "Data Science for Electric Power Systems"
Tetiana Bogodorova "Data Science for Electric Power Systems"Lviv Startup Club
 
Probabilistic data structures
Probabilistic data structuresProbabilistic data structures
Probabilistic data structuresYoav chernobroda
 
Sentiment Knowledge Discovery in Twitter Streaming Data
Sentiment Knowledge Discovery in Twitter Streaming DataSentiment Knowledge Discovery in Twitter Streaming Data
Sentiment Knowledge Discovery in Twitter Streaming DataAlbert Bifet
 
Pitfalls in benchmarking data stream classification and how to avoid them
Pitfalls in benchmarking data stream classification and how to avoid themPitfalls in benchmarking data stream classification and how to avoid them
Pitfalls in benchmarking data stream classification and how to avoid themAlbert Bifet
 
ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...Paolo Missier
 
Noura2
Noura2Noura2
Noura2Dr-mahmoud Algamel
 
Bring survey sampling techniques into big data
Bring survey sampling techniques into big dataBring survey sampling techniques into big data
Bring survey sampling techniques into big dataAntoine Rebecq
 
Linear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actionsLinear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actionsHesen Peng
 
Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)Antoine Rebecq
 
Efficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersEfficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersAlbert Bifet
 
Khaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled Eskaf
 
MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 Albert Bifet
 
Artificial intelligence and data stream mining
Artificial intelligence and data stream miningArtificial intelligence and data stream mining
Artificial intelligence and data stream miningAlbert Bifet
 
Scalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsScalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
 
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data StreamsFast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data StreamsAlbert Bifet
 
Chapter 13 finding relationships
Chapter 13 finding relationshipsChapter 13 finding relationships
Chapter 13 finding relationships201120305
 
A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...aimsnist
 
Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017SERC at Carleton College
 
eam2
eam2eam2
eam2butest
 
Slide 1
Slide 1Slide 1
Slide 1butest
 
Machine Learning: Machine Learning:
Machine Learning: Machine Learning:Machine Learning: Machine Learning:
Machine Learning: Machine Learning:butest
 
Data Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business DatabasesData Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business Databasesbutest
 
Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...butest
 
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentationbutest
 
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...Sezai Komur
 
Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...butest
 

More Related Content

What's hot

ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...Paolo Missier
 
Bring survey sampling techniques into big data
Bring survey sampling techniques into big dataBring survey sampling techniques into big data
Bring survey sampling techniques into big dataAntoine Rebecq
 
Linear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actionsLinear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actionsHesen Peng
 
Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)Antoine Rebecq
 
Efficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersEfficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersAlbert Bifet
 
Khaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled Eskaf
 
MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 Albert Bifet
 
Artificial intelligence and data stream mining
Artificial intelligence and data stream miningArtificial intelligence and data stream mining
Artificial intelligence and data stream miningAlbert Bifet
 
Scalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsScalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
 
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data StreamsFast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data StreamsAlbert Bifet
 
Chapter 13 finding relationships
Chapter 13 finding relationshipsChapter 13 finding relationships
Chapter 13 finding relationships201120305
 
A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...aimsnist
 
Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017SERC at Carleton College
 

What's hot (14)

ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...ReComp: challenges in selective recomputation of (expensive) data analytics t...
ReComp: challenges in selective recomputation of (expensive) data analytics t...
 
Noura2
Noura2Noura2
Noura2
 
Bring survey sampling techniques into big data
Bring survey sampling techniques into big dataBring survey sampling techniques into big data
Bring survey sampling techniques into big data
 
Linear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actionsLinear regression on 1 terabytes of data? Some crazy observations and actions
Linear regression on 1 terabytes of data? Some crazy observations and actions
 
Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)Sampling graphs efficiently - MAD Stat (TSE)
Sampling graphs efficiently - MAD Stat (TSE)
 
Efficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersEfficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream Classifiers
 
Khaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled eskaf presentation predicting power consumption using genetic algorithm
Khaled eskaf presentation predicting power consumption using genetic algorithm
 
MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016
 
Artificial intelligence and data stream mining
Artificial intelligence and data stream miningArtificial intelligence and data stream mining
Artificial intelligence and data stream mining
 
Scalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsScalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
Scalable and Efficient Algorithms for Analysis of Massive, Streaming Graphs
 
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data StreamsFast Perceptron Decision Tree Learning from Evolving Data Streams
Fast Perceptron Decision Tree Learning from Evolving Data Streams
 
Chapter 13 finding relationships
Chapter 13 finding relationshipsChapter 13 finding relationships
Chapter 13 finding relationships
 
A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...A Framework and Infrastructure for Uncertainty Quantification and Management ...
A Framework and Infrastructure for Uncertainty Quantification and Management ...
 
Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017Developing Computational Skills in the Sciences with Matlab Webinar 2017
Developing Computational Skills in the Sciences with Matlab Webinar 2017
 

Viewers also liked

Slide 1
Slide 1Slide 1
Slide 1butest
 
Machine Learning: Machine Learning:
Machine Learning: Machine Learning:Machine Learning: Machine Learning:
Machine Learning: Machine Learning:butest
 
Data Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business DatabasesData Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business Databasesbutest
 
Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...butest
 
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentationbutest
 
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...Sezai Komur
 
Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...butest
 
Semantic Search at Yahoo
Semantic Search at YahooSemantic Search at Yahoo
Semantic Search at YahooPeter Mika
 

Viewers also liked (9)

eam2
eam2eam2
eam2
 
Slide 1
Slide 1Slide 1
Slide 1
 
Machine Learning: Machine Learning:
Machine Learning: Machine Learning:Machine Learning: Machine Learning:
Machine Learning: Machine Learning:
 
Data Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business DatabasesData Mining and Knowledge Discovery in Business Databases
Data Mining and Knowledge Discovery in Business Databases
 
Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...Competitive advantage from Data Mining: some lessons learnt ...
Competitive advantage from Data Mining: some lessons learnt ...
 
2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation2/24(Wed) - PowerPoint Presentation
2/24(Wed) - PowerPoint Presentation
 
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
SP24 - Using the Content Enrichment Web Service with SharePoint Server 2013 ...
 
Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...Catégorisation automatisée de contenus documentaires : la ...
Catégorisation automatisée de contenus documentaires : la ...
 
Semantic Search at Yahoo
Semantic Search at YahooSemantic Search at Yahoo
Semantic Search at Yahoo
 

Similar to PPT slides

Random Forest Ensemble of Support Vector Regression for Solar Power Forecasting
Random Forest Ensemble of Support Vector Regression for Solar Power ForecastingRandom Forest Ensemble of Support Vector Regression for Solar Power Forecasting
Random Forest Ensemble of Support Vector Regression for Solar Power ForecastingMohamed Abuella
 
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...IRJET Journal
 
Parallelisation of the PC Algorithm (CAEPIA2015)
Parallelisation of the PC Algorithm (CAEPIA2015)Parallelisation of the PC Algorithm (CAEPIA2015)
Parallelisation of the PC Algorithm (CAEPIA2015)AMIDST Toolbox
 
Machine learning and_neural_network_lecture_slide_ece_dku
Machine learning and_neural_network_lecture_slide_ece_dkuMachine learning and_neural_network_lecture_slide_ece_dku
Machine learning and_neural_network_lecture_slide_ece_dkuSeokhyun Yoon
 
Enhancing the performance of Naive Bayesian Classifier using Information Gain...
Enhancing the performance of Naive Bayesian Classifier using Information Gain...Enhancing the performance of Naive Bayesian Classifier using Information Gain...
Enhancing the performance of Naive Bayesian Classifier using Information Gain...Rafiul Sabbir
 
IDA 2015: Efficient model selection for regularized classification by exploit...
IDA 2015: Efficient model selection for regularized classification by exploit...IDA 2015: Efficient model selection for regularized classification by exploit...
IDA 2015: Efficient model selection for regularized classification by exploit...George Balikas
 
Unsupervised Learning and Image Classification in High Performance Computing ...
Unsupervised Learning and Image Classification in High Performance Computing ...Unsupervised Learning and Image Classification in High Performance Computing ...
Unsupervised Learning and Image Classification in High Performance Computing ...HPCC Systems
 
The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...Anubhav Jain
 
PyData Miami 2019, Quantum Generalized Linear Models
PyData Miami 2019, Quantum Generalized Linear ModelsPyData Miami 2019, Quantum Generalized Linear Models
PyData Miami 2019, Quantum Generalized Linear ModelsColleen Farrelly
 
IRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET Journal
 
IRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET Journal
 
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
 
IRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET Journal
 
IRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET Journal
 
Flavours of Physics Challenge: Transfer Learning approach
Flavours of Physics Challenge: Transfer Learning approachFlavours of Physics Challenge: Transfer Learning approach
Flavours of Physics Challenge: Transfer Learning approachAlexander Rakhlin
 
Off-Policy Deep Reinforcement Learning without Exploration.pdf
Off-Policy Deep Reinforcement Learning without Exploration.pdfOff-Policy Deep Reinforcement Learning without Exploration.pdf
Off-Policy Deep Reinforcement Learning without Exploration.pdfPo-Chuan Chen
 
A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...o_almasi
 
Cost optimized reliability test planning rev 7
Cost optimized reliability test planning rev 7Cost optimized reliability test planning rev 7
Cost optimized reliability test planning rev 7ASQ Reliability Division
 
Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS TECSI FEA USP
 
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...Charles Martin
 

Similar to PPT slides (20)

Random Forest Ensemble of Support Vector Regression for Solar Power Forecasting
Random Forest Ensemble of Support Vector Regression for Solar Power ForecastingRandom Forest Ensemble of Support Vector Regression for Solar Power Forecasting
Random Forest Ensemble of Support Vector Regression for Solar Power Forecasting
 
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...
IRJET- Expert Independent Bayesian Data Fusion and Decision Making Model for ...
 
Parallelisation of the PC Algorithm (CAEPIA2015)
Parallelisation of the PC Algorithm (CAEPIA2015)Parallelisation of the PC Algorithm (CAEPIA2015)
Parallelisation of the PC Algorithm (CAEPIA2015)
 
Machine learning and_neural_network_lecture_slide_ece_dku
Machine learning and_neural_network_lecture_slide_ece_dkuMachine learning and_neural_network_lecture_slide_ece_dku
Machine learning and_neural_network_lecture_slide_ece_dku
 
Enhancing the performance of Naive Bayesian Classifier using Information Gain...
Enhancing the performance of Naive Bayesian Classifier using Information Gain...Enhancing the performance of Naive Bayesian Classifier using Information Gain...
Enhancing the performance of Naive Bayesian Classifier using Information Gain...
 
IDA 2015: Efficient model selection for regularized classification by exploit...
IDA 2015: Efficient model selection for regularized classification by exploit...IDA 2015: Efficient model selection for regularized classification by exploit...
IDA 2015: Efficient model selection for regularized classification by exploit...
 
Unsupervised Learning and Image Classification in High Performance Computing ...
Unsupervised Learning and Image Classification in High Performance Computing ...Unsupervised Learning and Image Classification in High Performance Computing ...
Unsupervised Learning and Image Classification in High Performance Computing ...
 
The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...The Status of ML Algorithms for Structure-property Relationships Using Matb...
The Status of ML Algorithms for Structure-property Relationships Using Matb...
 
PyData Miami 2019, Quantum Generalized Linear Models
PyData Miami 2019, Quantum Generalized Linear ModelsPyData Miami 2019, Quantum Generalized Linear Models
PyData Miami 2019, Quantum Generalized Linear Models
 
IRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms Comparison
 
IRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms ComparisonIRJET- Supervised Learning Classification Algorithms Comparison
IRJET- Supervised Learning Classification Algorithms Comparison
 
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
 
IRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification Algorithms
 
IRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification AlgorithmsIRJET- Performance Evaluation of Various Classification Algorithms
IRJET- Performance Evaluation of Various Classification Algorithms
 
Flavours of Physics Challenge: Transfer Learning approach
Flavours of Physics Challenge: Transfer Learning approachFlavours of Physics Challenge: Transfer Learning approach
Flavours of Physics Challenge: Transfer Learning approach
 
Off-Policy Deep Reinforcement Learning without Exploration.pdf
Off-Policy Deep Reinforcement Learning without Exploration.pdfOff-Policy Deep Reinforcement Learning without Exploration.pdf
Off-Policy Deep Reinforcement Learning without Exploration.pdf
 
A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...
 
Cost optimized reliability test planning rev 7
Cost optimized reliability test planning rev 7Cost optimized reliability test planning rev 7
Cost optimized reliability test planning rev 7
 
Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS
 
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...
Statistical Mechanics Methods for Discovering Knowledge from Production-Scale...
 

More from butest

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEbutest
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jacksonbutest
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALbutest
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer IIbutest
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.docbutest
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1butest
 
Facebook
Facebook Facebook
Facebook butest
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...butest
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTbutest
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docbutest
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docbutest
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.docbutest
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!butest
 

More from butest (20)

EL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBEEL MODELO DE NEGOCIO DE YOUTUBE
EL MODELO DE NEGOCIO DE YOUTUBE
 
1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同1. MPEG I.B.P frame之不同
1. MPEG I.B.P frame之不同
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Timeline: The Life of Michael Jackson
Timeline: The Life of Michael JacksonTimeline: The Life of Michael Jackson
Timeline: The Life of Michael Jackson
 
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...
 
LESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIALLESSONS FROM THE MICHAEL JACKSON TRIAL
LESSONS FROM THE MICHAEL JACKSON TRIAL
 
Com 380, Summer II
Com 380, Summer IICom 380, Summer II
Com 380, Summer II
 
PPT
PPTPPT
PPT
 
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet JazzThe MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazz
 
MICHAEL JACKSON.doc
MICHAEL JACKSON.docMICHAEL JACKSON.doc
MICHAEL JACKSON.doc
 
Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1Social Networks: Twitter Facebook SL - Slide 1
Social Networks: Twitter Facebook SL - Slide 1
 
Facebook
Facebook Facebook
Facebook
 
Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...Executive Summary Hare Chevrolet is a General Motors dealership ...
Executive Summary Hare Chevrolet is a General Motors dealership ...
 
Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...Welcome to the Dougherty County Public Library's Facebook and ...
Welcome to the Dougherty County Public Library's Facebook and ...
 
NEWS ANNOUNCEMENT
NEWS ANNOUNCEMENTNEWS ANNOUNCEMENT
NEWS ANNOUNCEMENT
 
C-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.docC-2100 Ultra Zoom.doc
C-2100 Ultra Zoom.doc
 
MAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.docMAC Printing on ITS Printers.doc.doc
MAC Printing on ITS Printers.doc.doc
 
Mac OS X Guide.doc
Mac OS X Guide.docMac OS X Guide.doc
Mac OS X Guide.doc
 
hier
hierhier
hier
 
WEB DESIGN!
WEB DESIGN!WEB DESIGN!
WEB DESIGN!
 

PPT slides

  • 1. Relational Learning with Gaussian Processes By Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S.Sathiya Keerthi (Columbia, Chicago, Cambridge, Yahoo!) Presented by Nesreen Ahmed, Nguyen Cao, Sebastian Moreno, Philip Schatz
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. Relational Gaussian Processes 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 where is a nxn matrix with four non-zero entries augmented from
  • 9.
  • 10.
  • 11. Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1
  • 12. Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1 Nearest Neighborhood K=1
  • 13. Semi supervised learning 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 ? ? ? ? ? ? ? ? -1 1 1 ? -1 Nearest Neighborhood K=2
  • 14.
  • 15.
  • 16.
  • 17. 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 30 Samples collected from a gaussian mixture with two components on the x-axis. Two labeled samples indicated by diamond and circle. K=3 Best value =0.4 based on approximate model evidence Results
  • 18. 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 Posterior Covariance matrix of RGP learnt from the data It captures the density information of unlabelled data Using the posterior covariance matrix learnt from the data as the new prior, supervised learning is carried out Curves represent predictive distribution for each class Results
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. 12/02/08 CS590M: Statistical Machine Learning - Fall 2008 Thanks Questions ?