Using support vector machine with a hybrid feature selection method to the st...lolokikipipi
This document discusses using a support vector machine (SVM) with a hybrid feature selection method to predict stock trends. It proposes using F-score filtering followed by a wrapper method called Supported Sequential Forward Search (SSFS) to select optimal features for the SVM. An experiment applies this approach to NASDAQ index data, reducing 30 features to 17 using F_SSFS and achieving a classification accuracy of 81.7% with the SVM, outperforming a backpropagation neural network. The hybrid approach helps address overfitting issues while improving the SVM's prediction performance.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Feature selection is the process of selecting a subset of relevant features for model construction. It reduces complexity and can improve or maintain model accuracy. The curse of dimensionality means that as the number of features increases, the amount of data needed to maintain accuracy also increases exponentially. Feature selection methods include filter methods (statistical tests for correlation), wrapper methods (using the model to select features), and embedded methods (combining filter and wrapper approaches). Common filter methods include linear discriminant analysis, analysis of variance, chi-square tests, and Pearson correlation. Wrapper methods use techniques like forward selection, backward elimination, and recursive feature elimination. Embedded methods dynamically select features based on inferences from previous models.
This document discusses feature selection algorithms, specifically branch and bound and beam search algorithms. It provides an overview of feature selection, discusses the fundamentals and objectives of feature selection. It then goes into more detail about how branch and bound works, including pseudocode, a flowchart, and an example. It also discusses beam search and compares branch and bound to other algorithms. In summary, it thoroughly explains branch and bound and beam search algorithms for performing feature selection on datasets.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.
Using support vector machine with a hybrid feature selection method to the st...lolokikipipi
This document discusses using a support vector machine (SVM) with a hybrid feature selection method to predict stock trends. It proposes using F-score filtering followed by a wrapper method called Supported Sequential Forward Search (SSFS) to select optimal features for the SVM. An experiment applies this approach to NASDAQ index data, reducing 30 features to 17 using F_SSFS and achieving a classification accuracy of 81.7% with the SVM, outperforming a backpropagation neural network. The hybrid approach helps address overfitting issues while improving the SVM's prediction performance.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Feature selection is the process of selecting a subset of relevant features for model construction. It reduces complexity and can improve or maintain model accuracy. The curse of dimensionality means that as the number of features increases, the amount of data needed to maintain accuracy also increases exponentially. Feature selection methods include filter methods (statistical tests for correlation), wrapper methods (using the model to select features), and embedded methods (combining filter and wrapper approaches). Common filter methods include linear discriminant analysis, analysis of variance, chi-square tests, and Pearson correlation. Wrapper methods use techniques like forward selection, backward elimination, and recursive feature elimination. Embedded methods dynamically select features based on inferences from previous models.
This document discusses feature selection algorithms, specifically branch and bound and beam search algorithms. It provides an overview of feature selection, discusses the fundamentals and objectives of feature selection. It then goes into more detail about how branch and bound works, including pseudocode, a flowchart, and an example. It also discusses beam search and compares branch and bound to other algorithms. In summary, it thoroughly explains branch and bound and beam search algorithms for performing feature selection on datasets.
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
An introduction to variable and feature selectionMarco Meoni
Presentation of a great paper from Isabelle Guyon (Clopinet) and André Elisseeff (Max Planck Institute) back in 2003, which outlines the main techniques for feature selection and model validation in machine learning systems
This document discusses feature selection concepts and methods. It defines features as attributes that determine which class an instance belongs to. Feature selection aims to select a relevant subset of features by removing irrelevant, redundant and unnecessary data. This improves learning accuracy, model performance and interpretability. The document categorizes feature selection algorithms as filter, wrapper or embedded methods based on how they evaluate feature subsets. It also discusses concepts like feature relevance, search strategies, successor generation and evaluation measures used in feature selection algorithms.
Evolving Reinforcement Learning Algorithms, JD. Co-Reyes et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Evolving Reinforcement Learning Algorithms 논문 내용을 정리해 발표했습니다. 이 논문은 Value-based Model-free RL 에이전트의 손실 함수를 표현하는 언어를 설계하고 기존 DQN보다 최적화된 손실 함수를 제안합니다. 많은 분들에게 도움이 되었으면 합니다.
In this talk, I have explained about feature selection, extraction with emphasis to image processing. Methods such as Principal Component Analysis, Canonical ANalysis are explained with numerical examples.
This document introduces algorithms and programming basics for Key Stage 3 students. It defines an algorithm as a set of step-by-step instructions to complete a task and notes they are not computer programs. Algorithms help design computer code by using flowcharts or pseudocode to visualize steps. Programming involves writing code in a language computers understand, using concepts like sequence, selection, and iteration. Examples show designing algorithms for everyday tasks and writing a simple program that declares variables and uses conditional selection and iteration.
This document provides an introduction to machine learning concepts including supervised learning, models for supervised learning such as decision trees, k-nearest neighbors, naive bayes, logistic regression, artificial neural networks, and support vector machines. It discusses evaluation metrics, choosing suitable models, and challenges such as finding a 100% accurate model. It also provides a case study example of predictive demographic modeling.
Optimization Technique for Feature Selection and Classification Using Support...IJTET Journal
Abstract— Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data Mining Performance gets reduced by Irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives are maximizing the classification performance and minimizing the number of features. Moreover, the existing feature selection algorithms treat the task as a single objective problem. Selecting attribute is done by the combination of attribute evaluator and search method using WEKA Machine Learning Tool. We compare SVM classification algorithm to automatically classify the data using selected features with different standard dataset.
Feature Selection Techniques for Software Fault Prediction (Summary)SungdoGu
This document discusses feature selection techniques for software fault prediction. It begins by motivating the need for feature selection when building defect prediction models using large sets of software metrics. It then describes common feature selection techniques like filter and wrapper methods. It provides examples of widely used software metrics like CK and McCabe & Halstead metrics. The document also analyzes threshold-based feature selection and evaluates its stability. Finally, it proposes a hybrid feature selection model and demonstrates its effectiveness on a dataset from the Eclipse project.
Network Based Intrusion Detection System using Filter Based Feature Selection...IRJET Journal
This document proposes a mutual information-based feature selection algorithm to select optimal features for network intrusion detection classification. The algorithm aims to handle dependent data features better than previous methods. It evaluates the effectiveness of the algorithm on network intrusion detection cases. Most previous methods suffer from low detection rates and high false alarm rates. The proposed approach uses feature selection, filtering, clustering, and clustering ensemble techniques in a hybrid data mining method to achieve high accuracy for intrusion detection systems.
Continuous Control with Deep Reinforcement Learning, lillicrap et al, 2015Chris Ohk
The paper introduces Deep Deterministic Policy Gradient (DDPG), a model-free reinforcement learning algorithm for problems with continuous action spaces. DDPG combines actor-critic methods with experience replay and target networks similar to DQN. It uses a replay buffer to minimize correlations between samples and target networks to provide stable learning targets. The algorithm was able to solve challenging control problems with high-dimensional observation and action spaces, demonstrating the ability of deep reinforcement learning to handle complex, continuous control tasks.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
Adversarially Guided Actor-Critic, Y. Flet-Berliac et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Adversarially Guided Actor-Critic 논문 내용을 정리해 발표했습니다. AGAC는 Actor-Critic에 GAN에서 영감을 받은 방법들을 결합해 리워드가 희소하고 탐험이 어려운 환경에서 뛰어난 성능을 보여줍니다. 많은 분들에게 도움이 되었으면 합니다.
This document discusses the fundamentals of fuzzy logic control systems. It begins by defining fuzzy logic as a problem-solving control system methodology that uses linguistic variables and fuzzy rules to map inputs to outputs. It then outlines the typical elements of a fuzzy logic system, including fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and defuzzification. Finally, it provides an example of applying fuzzy logic to control the temperature in a simple heating/cooling system.
This document discusses feature selection techniques for classification problems. It begins by outlining class separability measures like divergence, Bhattacharyya distance, and scatter matrices. It then discusses feature subset selection approaches, including scalar feature selection which treats features individually, and feature vector selection which considers feature sets and correlations. Examples are provided to demonstrate calculating class separability measures for different feature combinations on sample datasets. Exhaustive search and suboptimal techniques like forward, backward, and floating selection are discussed for choosing optimal feature subsets. The goal of feature selection is to select a subset of features that maximizes class separation.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
Kaggle Higgs Boson Machine Learning ChallengeBernard Ong
What It Took to Score the Top 2% on the Higgs Boson Machine Learning Challenge. A journey into advanced machine learning models ensembles stacking methods.
The document discusses query processing and optimization. It describes the basic concepts including query processing, query optimization, and the phases of query processing. It also explains relational algebra operations like selection, projection, joins, and additional operations. The document then covers topics like query decomposition, analysis, normalization, simplification, and restructuring during query optimization. It discusses cost estimation and algorithms for implementing relational algebra operations and file organization.
Using FME for Topographical Data Generalization at Natural Resources CanadaSafe Software
To meet increasing and diversified user needs for geographic information, Natural Resources Canada (NRCan) must produce and maintain geographic data at multiple scales. To automate the generalization process NRCan is using an approach based on FME and MetaAlgorithms.
Meetup_Consumer_Credit_Default_Vers_2_AllBernard Ong
The document discusses a team's approach to a Kaggle challenge to predict consumer credit default. It outlines their goals, dataset details, modeling strategy using an agile process, and key results. Their parallel modeling approach included feature analysis, single/ensemble models, stacking, voting classifiers, and Bayesian optimization. Their top-scoring model achieved an AUC of 0.88912, placing first on the private Kaggle leaderboard. Lessons included the importance of cross-validation, hyperparameter tuning, and following an agile process.
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
Scaling out logistic regression with SparkBarak Gitsis
This document discusses scaling out logistic regression with Apache Spark. It describes the need to classify a large number of websites using machine learning. Several approaches to logistic regression were tried, including a single machine Java implementation and moving to Spark for better scalability. Spark's L-BFGS algorithm was chosen for its out of the box distributed logistic regression solution. Challenges implementing logistic regression at large scale are discussed, such as overfitting and regularization. Methods used to address these challenges include L2 regularization, cross-validation to select the regularization parameter, and extensions made to Spark's LBFGS implementation.
This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.
This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.
Evolving Reinforcement Learning Algorithms, JD. Co-Reyes et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Evolving Reinforcement Learning Algorithms 논문 내용을 정리해 발표했습니다. 이 논문은 Value-based Model-free RL 에이전트의 손실 함수를 표현하는 언어를 설계하고 기존 DQN보다 최적화된 손실 함수를 제안합니다. 많은 분들에게 도움이 되었으면 합니다.
In this talk, I have explained about feature selection, extraction with emphasis to image processing. Methods such as Principal Component Analysis, Canonical ANalysis are explained with numerical examples.
This document introduces algorithms and programming basics for Key Stage 3 students. It defines an algorithm as a set of step-by-step instructions to complete a task and notes they are not computer programs. Algorithms help design computer code by using flowcharts or pseudocode to visualize steps. Programming involves writing code in a language computers understand, using concepts like sequence, selection, and iteration. Examples show designing algorithms for everyday tasks and writing a simple program that declares variables and uses conditional selection and iteration.
This document provides an introduction to machine learning concepts including supervised learning, models for supervised learning such as decision trees, k-nearest neighbors, naive bayes, logistic regression, artificial neural networks, and support vector machines. It discusses evaluation metrics, choosing suitable models, and challenges such as finding a 100% accurate model. It also provides a case study example of predictive demographic modeling.
Optimization Technique for Feature Selection and Classification Using Support...IJTET Journal
Abstract— Classification problems often have a large number of features in the data sets, but only some of them are useful for classification. Data Mining Performance gets reduced by Irrelevant and redundant features. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main objectives are maximizing the classification performance and minimizing the number of features. Moreover, the existing feature selection algorithms treat the task as a single objective problem. Selecting attribute is done by the combination of attribute evaluator and search method using WEKA Machine Learning Tool. We compare SVM classification algorithm to automatically classify the data using selected features with different standard dataset.
Feature Selection Techniques for Software Fault Prediction (Summary)SungdoGu
This document discusses feature selection techniques for software fault prediction. It begins by motivating the need for feature selection when building defect prediction models using large sets of software metrics. It then describes common feature selection techniques like filter and wrapper methods. It provides examples of widely used software metrics like CK and McCabe & Halstead metrics. The document also analyzes threshold-based feature selection and evaluates its stability. Finally, it proposes a hybrid feature selection model and demonstrates its effectiveness on a dataset from the Eclipse project.
Network Based Intrusion Detection System using Filter Based Feature Selection...IRJET Journal
This document proposes a mutual information-based feature selection algorithm to select optimal features for network intrusion detection classification. The algorithm aims to handle dependent data features better than previous methods. It evaluates the effectiveness of the algorithm on network intrusion detection cases. Most previous methods suffer from low detection rates and high false alarm rates. The proposed approach uses feature selection, filtering, clustering, and clustering ensemble techniques in a hybrid data mining method to achieve high accuracy for intrusion detection systems.
Continuous Control with Deep Reinforcement Learning, lillicrap et al, 2015Chris Ohk
The paper introduces Deep Deterministic Policy Gradient (DDPG), a model-free reinforcement learning algorithm for problems with continuous action spaces. DDPG combines actor-critic methods with experience replay and target networks similar to DQN. It uses a replay buffer to minimize correlations between samples and target networks to provide stable learning targets. The algorithm was able to solve challenging control problems with high-dimensional observation and action spaces, demonstrating the ability of deep reinforcement learning to handle complex, continuous control tasks.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
Adversarially Guided Actor-Critic, Y. Flet-Berliac et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Adversarially Guided Actor-Critic 논문 내용을 정리해 발표했습니다. AGAC는 Actor-Critic에 GAN에서 영감을 받은 방법들을 결합해 리워드가 희소하고 탐험이 어려운 환경에서 뛰어난 성능을 보여줍니다. 많은 분들에게 도움이 되었으면 합니다.
This document discusses the fundamentals of fuzzy logic control systems. It begins by defining fuzzy logic as a problem-solving control system methodology that uses linguistic variables and fuzzy rules to map inputs to outputs. It then outlines the typical elements of a fuzzy logic system, including fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and defuzzification. Finally, it provides an example of applying fuzzy logic to control the temperature in a simple heating/cooling system.
This document discusses feature selection techniques for classification problems. It begins by outlining class separability measures like divergence, Bhattacharyya distance, and scatter matrices. It then discusses feature subset selection approaches, including scalar feature selection which treats features individually, and feature vector selection which considers feature sets and correlations. Examples are provided to demonstrate calculating class separability measures for different feature combinations on sample datasets. Exhaustive search and suboptimal techniques like forward, backward, and floating selection are discussed for choosing optimal feature subsets. The goal of feature selection is to select a subset of features that maximizes class separation.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
Kaggle Higgs Boson Machine Learning ChallengeBernard Ong
What It Took to Score the Top 2% on the Higgs Boson Machine Learning Challenge. A journey into advanced machine learning models ensembles stacking methods.
The document discusses query processing and optimization. It describes the basic concepts including query processing, query optimization, and the phases of query processing. It also explains relational algebra operations like selection, projection, joins, and additional operations. The document then covers topics like query decomposition, analysis, normalization, simplification, and restructuring during query optimization. It discusses cost estimation and algorithms for implementing relational algebra operations and file organization.
Using FME for Topographical Data Generalization at Natural Resources CanadaSafe Software
To meet increasing and diversified user needs for geographic information, Natural Resources Canada (NRCan) must produce and maintain geographic data at multiple scales. To automate the generalization process NRCan is using an approach based on FME and MetaAlgorithms.
Meetup_Consumer_Credit_Default_Vers_2_AllBernard Ong
The document discusses a team's approach to a Kaggle challenge to predict consumer credit default. It outlines their goals, dataset details, modeling strategy using an agile process, and key results. Their parallel modeling approach included feature analysis, single/ensemble models, stacking, voting classifiers, and Bayesian optimization. Their top-scoring model achieved an AUC of 0.88912, placing first on the private Kaggle leaderboard. Lessons included the importance of cross-validation, hyperparameter tuning, and following an agile process.
Optimal feature selection from v mware esxi 5.1 feature setijccmsjournal
A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which
suggest usage of different resources of the server. Feature selection algorithms have been used to extract
the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using esxtop command.
Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index
respectively. The best cluster is further identified by the determined indices. The features of the best cluster
are considered into a set of optimal parameters.
Scaling out logistic regression with SparkBarak Gitsis
This document discusses scaling out logistic regression with Apache Spark. It describes the need to classify a large number of websites using machine learning. Several approaches to logistic regression were tried, including a single machine Java implementation and moving to Spark for better scalability. Spark's L-BFGS algorithm was chosen for its out of the box distributed logistic regression solution. Challenges implementing logistic regression at large scale are discussed, such as overfitting and regularization. Methods used to address these challenges include L2 regularization, cross-validation to select the regularization parameter, and extensions made to Spark's LBFGS implementation.
This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.
This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.
Ensemble methods combine multiple learners to create more reliable predictions than single learners. Bagging assigns bootstrap data to classifiers and averages predictions, while boosting assigns weak learners and updates weights to correct errors. XGBoost is an optimized gradient boosting algorithm that offers excellent performance, faster execution than GBM, and various utilities like early stopping and regularization. It has Python and scikit-learn wrappers for easy integration and hyperparameter tuning.
2015-06-15 Large-Scale Elastic-Net Regularized Generalized Linear Models at S...DB Tsai
Nonlinear methods are widely used to produce higher performance compared with linear methods; however, nonlinear methods are generally more expensive in model size, training time, and scoring phase. With proper feature engineering techniques like polynomial expansion, the linear methods can be as competitive as those nonlinear methods. In the process of mapping the data to higher dimensional space, the linear methods will be subject to overfitting and instability of coefficients which can be addressed by penalization methods including Lasso and Elastic-Net. Finally, we'll show how to train linear models with Elastic-Net regularization using MLlib.
Several learning algorithms such as kernel methods, decision tress, and random forests are nonlinear approaches which are widely used to have better performance compared with linear methods. However, with feature engineering techniques like polynomial expansion by mapping the data into a higher dimensional space, the performance of linear methods can be as competitive as those nonlinear methods. As a result, linear methods remain to be very useful given that the training time of linear methods is significantly faster than the nonlinear ones, and the model is just a simple small vector which makes the prediction step very efficient and easy. However, by mapping the data into higher dimensional space, those linear methods are subject to overfitting and instability of coefficients, and those issues can be successfully addressed by penalization methods including Lasso and Elastic-Net. Lasso method with L1 penalty tends to result in many coefficients shrunk exactly to zero and a few other coefficients with comparatively little shrinkage. L2 penalty trends to result in all small but non-zero coefficients. Combining L1 and L2 penalties are called Elastic-Net method which tends to give a result in between. In the first part of the talk, we'll give an overview of linear methods including commonly used formulations and optimization techniques such as L-BFGS and OWLQN. In the second part of talk, we will talk about how to train linear models with Elastic-Net using our recent contribution to Spark MLlib. We'll also talk about how linear models are practically applied with big dataset, and how polynomial expansion can be used to dramatically increase the performance.
DB Tsai is an Apache Spark committer and a Senior Research Engineer at Netflix. He is recently working with Apache Spark community to add several new algorithms including Linear Regression and Binary Logistic Regression with ElasticNet (L1/L2) regularization, Multinomial Logistic Regression, and LBFGS optimizer. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where he developed innovative large-scale distributed linear algorithms, and then contributed back to open source Apache Spark project.
This document discusses sparse linear models and Bayesian variable selection. It introduces the spike and slab model for Bayesian variable selection, which uses a binary vector γ to indicate whether features are relevant or not. Computing the posterior p(γ|D) involves calculating the marginal likelihood p(D|γ). Greedy search and stochastic search methods are discussed to approximate the posterior over models. L1 regularization, also known as lasso, is introduced as an optimization technique since computing the posterior for discrete γ is difficult. Lasso replaces the discrete priors with continuous priors to encourage sparsity. Coordinate descent is discussed as an algorithm to optimize the lasso objective function.
This document discusses Bayesian global optimization and its application to tuning machine learning models. It begins by outlining some of the challenges of tuning ML models, such as the non-intuitive nature of the task. It then introduces Bayesian global optimization as an approach to efficiently search the hyperparameter space to find optimal configurations. The key aspects of Bayesian global optimization are described, including using Gaussian processes to build models of the objective function from sampled points and finding the next best point to sample via expected improvement. Several examples are provided demonstrating how Bayesian global optimization outperforms standard tuning methods in optimizing real-world ML tasks.
Scott Clark, Co-Founder and CEO, SigOpt at MLconf SF 2016MLconf
Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. We will motivate the problem and give example applications.
We will also talk about our development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. We will discuss how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.
We will end with an in-depth example of using these methods to tune the features and hyperparameters of a real world problem and give several real world applications.
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...Spark Summit
This document discusses using regularized linear models like logistic regression with feature engineering techniques like polynomial expansion to solve classification problems in a scalable way. It describes how polynomial expansion can make nonlinear relationships linear by transforming features into higher dimensions. It also explains how Elastic Net regularization, which combines L1 and L2 penalties, can select important features and scale to large datasets using Apache Spark. Experiments on several datasets show logistic regression with degree-2 polynomial features performs comparably to nonlinear kernels while training faster.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Implementation of linear regression and logistic regression on SparkDalei Li
This presentation was developed for a course project at Technical University of Madrid. The course is massively parallel machine learning supervised by Alberto Mozo and Bruno Ordozgoiti.
This document outlines course material for a phylogenetics and sequence analysis course. It discusses building phylogenetic trees using distance, parsimony, and maximum likelihood methods. It also covers statistical methods like Bayesian phylogenetics for calculating trees. Software for building trees and summarizing results are presented, including MrBayes, BEAST, and DendroPy. The document provides guidance on evaluating convergence and summarizing Bayesian analyses. Model selection using programs like jModelTest and proper formatting of input sequence data are also covered.
2013.09.10 Giraph at London Hadoop Users GroupNitay Joffe
1) The document discusses scaling Apache Giraph, an open source graph computation engine. It outlines several problems that arise when scaling Giraph to large graphs, such as worker crashes and master crashes.
2) Solutions proposed to address these problems include checkpointing to handle worker crashes, using ZooKeeper for master queue handling to address master crashes, and using byte arrays and unsafe serialization to reduce object overhead.
3) Test results show Giraph can scale to graphs with billions of vertices and edges on a cluster of 50 workers, achieving speedups of 20x CPU and 100x elapsed time compared to Hive for similar graph computations.
WorkflowSim is a toolkit for simulating scientific workflows in distributed environments. It models workflow overhead, failures, and the hierarchical nature of workflows with tasks and jobs. WorkflowSim extends CloudSim to be workflow-aware and supports modeling diverse overhead distributions, failure models, and fault tolerant techniques like reclustering and job retry. It helps researchers evaluate workflow optimization techniques more accurately. Validation experiments show WorkflowSim can accurately simulate overhead and failures and their impact on workflow scheduling heuristics and fault tolerant clustering approaches.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
The document summarizes scaling Apache Giraph, an open source graph processing system. It discusses several problems that arise when scaling Giraph to large graphs, such as worker crashes, master crashes, primitive data structures causing overhead, and too many objects causing garbage collection issues. For each problem, it provides the solution Giraph uses, such as checkpointing, ZooKeeper for master coordination, using more efficient data structures like byte arrays instead of objects, and sharding aggregators. It also discusses optimization techniques like using Netty for networking and JVM profiling tools. The final result is Giraph can now process the entire Facebook graph in minutes instead of days.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Va...Dongmin Lee
I reviewed the PEARL paper.
PEARL (Probabilistic Embeddings for Actor-critic RL) is an off-policy meta-RL algorithm to achieve both meta-training and adaptation efficiency. It performs probabilistic encoder filtering of latent task variables to enables posterior sampling for structured and efficient exploration.
Outline
- Abstract
- Introduction
- Probabilistic Latent Context
- Off-Policy Meta-Reinforcement Learning
- Experiments
Link: https://arxiv.org/abs/1903.08254
Thank you!
Hyper Parameter Optimization(HPO) is the technique of tuning parameters to optimize learning algorithms performance on an independent dataset.
Wiki Definition: https://en.wikipedia.org/wiki/Hyperparameter_optimization
In this presentation, we highlight how one can use VW(vowpal wabbit) and Spark framework to achieve to build generalized models in a distributed way.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
variBAD, A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning.pptxSeungeon Baek
1) The document introduces a method called variBAD for Bayes-adaptive deep reinforcement learning via meta-learning. variBAD aims to solve the exploration-exploitation dilemma by learning a posterior distribution over task embeddings through meta-learning.
2) variBAD models the reward and transition functions conditioned on a task embedding variable, and infers the posterior over this variable given the agent's experience. This allows planning in the lower-dimensional embedding space instead of the full model space.
3) variBAD is trained end-to-end to maximize a model learning objective and policy gradient objective. Experiments show variBAD achieves better sample efficiency than prior methods on gridworld and MuJoCo tasks.
The document provides an overview of convex optimization problems, including linear programming (LP), quadratic programming (QP), quadratic constraint quadratic programming (QCQP), second-order cone programming (SOCP), and geometric programming. It discusses how these problems can be transformed into equivalent convex optimization problems to help solve them. Local optima are guaranteed to be global optima for convex problems. Optimality criteria are presented for problems with differentiable objectives.
AlphaGo Zero is a Go-playing program that was trained solely through self-play reinforcement learning without using any human data. It uses a single neural network that takes in raw board positions and outputs both a policy distribution over moves and a value estimate of each position. AlphaGo Zero incorporates lookahead search through Monte Carlo tree search that relies entirely on the neural network, without performing any rollouts. This allows it to achieve superior performance compared to previous AlphaGo versions, requiring less training time while developing novel strategies not seen in human experts.
The document discusses Deep Q-Network (DQN), which combines Q-learning with deep neural networks to allow for function approximation and solving problems with large state/action spaces. DQN uses experience replay and a separate target network to stabilize training. It has led to many successful variants, including Double DQN which reduces overestimation, prioritized experience replay which replays important transitions more frequently, and dueling networks which separate value and advantage estimation.
It's the deck for one Hulu internal machine learning workshop, which introduces the background, theory and application of expectation propagation method.
this is the forth slide for machine learning workshop in Hulu. Machine learning methods are summarized in the beginning of this slide, and boosting tree is introduced then. You are commended to try boosting tree when the feature number is not too much (<1000)
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
16. L2
VS.
L1
• L2
regulariza7on
– Almost
all
weights
are
not
equal
to
zero
– Not
suitable
when
training
samples
are
scarce
• L1
regulariza7on
– Produces
sparse
parameter
vectors
– More
suitable
when
most
features
are
irrelevant
– Could
handle
scarce
training
samples
be>er
17. Experiments
• Dataset
– Goal:
gender
predic7on
– Dataset:
train
samples
(431k),
test
samples
(167k)
• Comparison
algorithms
– A:
gradient
descent
with
L1
regulariza7on
– B:
gradient
descent
with
L2
regulariza7on
– C:
OWL-‐QN
(L-‐BFGS
based
op7miza7on
with
L1
regulariza7on)
• Parameters
choice
–
–
–
–
Regulariza7on
value
Step(learning
speed)
Decay
ra7o
Itera7on
over
condi7on
• Max
itera7on
7mes(50)
||
AUC
change
<=0.0005
18. Experiments
(cont.)
• Experiments
results
Parameters
and
metrics
gradient
descent
with
gradient
descent
with
L1
L2
OWL-‐QN
‘best’
regulariza7on
0.001~0.005
term
0.0002~0.001
1
Best
step
0.05
0.02~0.05
-‐
Best
decay
ra7o
0.85
0.85
-‐
Itera7on
7mes
26
20~26
48
Not
zero
feature
/
all
feature
10492/10938
10938/10938
6629/10938
AUC
0.8470
0.8463
0.8467
20. More
Link
func7ons
• Inference
with
maximize
likelihood
• Link
func7on
• Link
func7ons
for
binomial
distribu7on
– Logit
func7on
– Probit
func7on
– Log-‐log
func7on
21. Generalized
linear
model
•
What
is
GLM
– Generaliza7on
of
linear
regression
– Connect
linear
model
with
response
variable
by
link
func7on
– More
distribu7on
for
response
variable
•
Typical
GLM
•
Overview
– Linear
regression
,
Logis7c
regression,
Poisson
regression
22. Applica7on
• Yahoo
– <Personalized
Click
Predic7on
in
Sponsored
Search>
WSDM’10
• Microsoq
– <Scalable
Training
of
L1-‐Regularized
Log-‐Linear
Models>
ICML’07
• Baidu
– Contextual
ads
CTR
predic7on
• h>p://www.docin.com/p-‐376254439.html
• Hulu
–
–
–
–
Demographic
targe7ng
Other
ad-‐targe7ng
project
Custom
churn
predic7on
More…
23. Reference
• ‘Scalable
Training
of
L1-‐Regularized
Log-‐Linear
Models’
ICML’07
– h>p://www.docin.com/p-‐376254439.html#
• ‘Genera-ve
and
discrimina-ve
classifiers:
Naïve
Bayes
and
logis-c
regression’
by
Mitchell
• ‘Feature
selec-on,
L1
vs.
L2
regulariza-on,
and
rota-onal
invariance’
ICML’04
24. Recommended
resources
• Machine
Learning
open
class
–
by
Andrew
Ng
– //10.20.0.130/TempShare/Machine-‐Learning
Open
Class
• h>p://www.cnblogs.com/vivounicorn/archive/
2012/02/24/2365328.html
• logis7c
regression
Implementa7on[link]
– //10.20.0.130/TempShare/guodong/Logis7c
regression
Implementa7on/
– Support
binomial
and
mul7nominal
LR
with
L1
and
L2
regulariza7on
• OWL-‐QN
– //10.20.0.130/TempShare/guodong/OWL-‐QN/
Unsupervised learning(聚类,降维(topic model)): learn structure from unlabeled data. Closely related with density estimation; summarize the dataSemi-supervised learning: use both labeled and unlabeled samples for training; It’s cost to collect lots of labels sometimes, use both
Logistic regression is one of the most popular classifier.Advantage: 1. easy understand and implement; 2. not bad performance; 3. light weight and less time taken for training and prediction;(can handle large dataset) 4. easy parallelizationValue to attendances:Know about what is logistic regression, what’s the advantages and disadvantage. what kind of problems are suitable apply to.L1 and L2 regularizationHow to inference through maximize likelihood with gradient descent. And know how to implement it
对于generalized linear model,如果response variable是binomial或者multinomial分布,且选择了logit function作为link function 就是logistic regressionLogistic function 是logit function的反函数
Link function: (1) generalized linear model的重要组成部分:将linear regression拓展到generalized linear model;(2)link function的反函数的自变量介于(-无穷,+无穷),若y服从binominal分布,应变量介于【0,1】区间The inverse of any continuous cumulative distribution function (CDF) can be used for the link since the CDF’s range is [0,1]
Generalized linear model 广义上的线性模型,都有一个基本的线性单元W*X(linear regression),通过各种link function建立该线性单元和各种分布的response variable的关系。包含linear regression (normal distribution),logistic regression (binominal/multi-nominal distribution), Poisson regression (Poisson distribution)对于binominal/multi-nominal distribution,我们也可以选择除logit link function之外的link function (广义的logistic regression)