Machine learning in non-stationary environments : introduction to covariate shift adaptation

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Machine learning in non-stationary environments : introduction to covariate shift adaptation / Masashi Sugiyama and Motoaki Kawanabe

資料種別:

図書

出版情報:

Cambridge, Mass. ; London : MIT Press, c2012

形態:

xiv, 261 p. ; 24 cm

シリーズ名:

Adaptive computation and machine learning <BA35126370>

著者名:

分担著者名:

杉山, 将

ISBN:

9780262017091 [0262017091] (: hbk)

書誌ID:

BB08941717

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

注記:

Includes bibliographical references (p. [247]-257) and index

著者名ヨミ:

スギヤマ, マサシ

主題:

Machine learning

言語:

英語

目次情報:

Foreword

Preface

Introduction / I：

Introduction and Problem Formulation / 1：

Machine Learning under Covariate Shift / 1.1：

Quick Tour of Covariate Shift Adaptation / 1.2：

Problem Formulation / 1.3：

Function Learning from Examples / 1.3.1：

Loss Functions / 1.3.2：

Generalization Error / 1.3.3：

Covariate Shift / 1.3.4：

Models for Function Learning / 1.3.5：

Specification of Models / 1.3.6：

Structure of This Book / 1.4：

Part II: Learning under Covariate Shift / 1.4.1：

Part III: Learning Causing Covariate Shift / 1.4.2：

Learning Under Covariate Shift / II：

Function Approximation / 2：

Importance-Weighting Techniques for Covariate Shift Adaptation / 2.1：

Importance-Weighted ERM / 2.1.1：

Adaptive IWERM / 2.1.2：

Regularized IWERM / 2.1.3：

Examples of Importance-Weighted Regression Methods / 2.2：

Squared Loss: Least-Squares Regression / 2.2.1：

Absolute Loss: Least-Absolute Regression / 2.2.2：

Huber Loss: Huber Regression / 2.2.3：

Deadzone-Linear Loss: Support Vector Regression / 2.2.4：

Examples of Importance-Weighted Classification Methods / 2.3：

Squared Loss: Fisher Discriminant Analysis / 2.3.1：

Logistic Loss: Logistic Regression Classifier / 2.3.2：

Hinge Loss: Support Vector Machine / 2.3.3：

Exponential Loss: Boosting / 2.3.4：

Numerical Examples / 2.4：

Regression / 2.4.1：

Classification / 2.4.2：

Summary and Discussion / 2.5：

Model Selection / 3：

Importance-Weighted Akaike Information Criterion / 3.1：

Importance-Weighted Subspace Information Criterion / 3.2：

Input Dependence vs. Input Independence in Generalization Error Analysis / 3.2.1：

Approximately Correct Models / 3.2.2：

Input-Dependent Analysis of Generalization Error / 3.2.3：

Importance-Weighted Cross-Validation / 3.3：

Importance Estimation / 3.4：

Kernel Density Estimation / 4.1：

Kernel Mean Matching / 4.2：

Logistic Regression / 4.3：

Kullback-Leibler Importance Estimation Procedure / 4.4：

Algorithm / 4.4.1：

Model Selection by Cross-Validation / 4.4.2：

Basis Function Design / 4.4.3：

Least-Squares Importance Fitting / 4.5：

Basis Function Design and Model Selection / 4.5.1：

Regularization Path Tracking / 4.5.3：

Unconstrained Least-Squares Importance Fitting / 4.6：

Analytic Computation of Leave-One-Out Cross-Validation / 4.6.1：

Setting / 4.7：

Importance Estimation by KLIEP / 4.7.2：

Covariate Shift Adaptation by IWLS and IWCV / 4.7.3：

Experimental Comparison / 4.8：

Summary / 4.9：

Direct Density-Ratio Estimation with Dimensionality Reduction / 5：

Density Difference in Hetero-Distributional Subspace / 5.1：

Characterization of Hetero-Distributional Subspace / 5.2：

Identifying Hetero-Distributional Subspace / 5.3：

Basic Idea / 5.3.1：

Fisher Discriminant Analysis / 5.3.2：

Local Fisher Discriminant Analysis / 5.3.3：

Using LFDA for Finding Hetero-Distributional Subspace / 5.4：

Density-Ratio Estimation in the Hetero-Distributional Subspace / 5.5：

Illustrative Example / 5.6：

Performance Comparison Using Artificial Data Sets / 5.6.2：

Relation to Sample Selection Bias / 5.7：

Heckman's Sample Selection Model / 6.1：

Distributional Change and Sample Selection Bias / 6.2：

The Two-Step Algorithm / 6.3：

Relation to Covariate Shift Approach / 6.4：

Applications of Covariate Shift Adaptation / 7：

Brain-Computer Interface / 7.1：

Background / 7.1.1：

Experimental Setup / 7.1.2：

Experimental Results / 7.1.3：

Speaker Identification / 7.2：

Formulation / 7.2.1：

Natural Language Processing / 7.2.3：

Perceived Age Prediction from Face Images / 7.3.1：

Incorporating Characteristics of Human Age Perception / 7.4.1：

Human Activity Recognition from Accelerometric Data / 7.4.4：

Importance-Weighted Least-Squares Probabilistic Classifier / 7.5.1：

Experimental Results. / 7.5.3：

Sample Reuse in Reinforcement Learning / 7.6：

Markov Decision Problems / 7.6.1：

Policy Iteration / 7.6.2：

Value Function Approximation / 7.6.3：

Sample Reuse by Covariate Shift Adaptation / 7.6.4：

On-Policy vs. Off-Policy / 7.6.5：

Importance Weighting in Value Function Approximation / 7.6.6：

Automatic Selection of the Flattening Parameter / 7.6.7：

Sample Reuse Policy Iteration / 7.6.8：

Robot Control Experiments / 7.6.9：

Learning Causing Covariate Shift / III：

Active Learning / 8：

Preliminaries / 8.1：

Setup / 8.1.1：

Decomposition of Generalization Error / 8.1.2：

Basic Strategy of Active Learning / 8.1.3：

Population-Based Active Learning Methods / 8.2：

Classical Method of Active Learning for Correct Models / 8.2.1：

Limitations of Classical Approach and Countermeasures / 8.2.2：

Input-Independent Variance-Only Method / 8.2.3：

Input-Dependent Variance-Only Method / 8.2.4：

Input-Independent Bias-and-Variance Approach / 8.2.5：

Numerical Examples of Population-Based Active Learning Methods / 8.3：

Accuracy of Generalization Error Estimation / 8.3.1：

Obtained Generalization Error / 8.3.3：

Pool-Based Active Learning Methods / 8.4：

Classical Active Learning Method for Correct Models and Its Limitations / 8.4.1：

Numerical Examples of Pool-Based Active Learning Methods / 8.4.2：

Active Learning with Model Selection / 8.6：

Direct Approach and the Active Learning/Model Selection Dilemma / 9.1：

Sequential Approach / 9.2：

Batch Approach / 9.3：

Ensemble Active Learning / 9.4：

Analysis of Batch Approach / 9.5：

Analysis of Sequential Approach / 9.5.3：

Comparison of Obtained Generalization Error / 9.5.4：

Applications of Active Learning / 9.6：

Design of Efficient Exploration Strategies in Reinforcement Learning / 10.1：

Efficient Exploration with Active Learning / 10.1.1：

Reinforcement Learning Revisited / 10.1.2：

Estimating Generalization Error for Active Learning / 10.1.3：

Designing Sampling Policies / 10.1.5：

Active Learning in Policy Iteration / 10.1.6：

Wafer Alignment in Semiconductor Exposure Apparatus / 10.1.7：

Conclusions / IV：

Conclusions and Future Prospects / 11：

Future Prospects / 11.1：

Appendix: List of Symbols and Abbreviations

Bibliography

Index

Foreword

Preface

Introduction / I：

Introduction and Problem Formulation / 1：

Machine Learning under Covariate Shift / 1.1：

Quick Tour of Covariate Shift Adaptation / 1.2：

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