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1. Machine learning in non-stationary environments : introduction to covariate shift adaptation ((ebook) :)
Masashi Sugiyama and Motoaki Kawanabe
出版情報: Cambridge, Mass. ; London : MIT Press, c2012  1 online resource (xiv, 261 p.)
シリーズ名: Adaptive computation and machine learning ;
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目次情報: 続きを見る
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：
2.

電子ブック
ＥＢ
2. Dataset shift in machine learning ((ebook) :)
[edited by] Joaquin Quiñonero-Candela ... [et al.]
出版情報: Cambridge, Mass. ; London : MIT, c2009  1 online resource (xv, 229 p.)
シリーズ名: Neural information processing ;
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3.

電子ブック
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3. Foundations of machine learning. 2nd ed (: electronic bk)
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
出版情報: [Ann Arbor, Mich.] : ProQuest Ebook Central, [20--]  1 online resource
シリーズ名: Adaptive computation and machine learning
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4.

電子ブック
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4. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems. 2nd ed (: electronic bk)
Aurélien Géron
出版情報: Sebastopol, Calif. : O'Reilly, 2019  1 online resource (xxv, 819 p.)
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5.

電子ブック
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5. Probabilistic machine learning : an introduction (: electronic bk)
Kevin P. Murphy
出版情報: [Ann Arbor, Mich.] : ProQuest Ebook Central, [202-]  1 online resource
シリーズ名: Adaptive computation and machine learning
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6.

電子ブック
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6. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems. 3rd ed (: electronic bk)
Aurélien Géron
出版情報: [S.l.] : EBSCOhost, [20--]  1 online resource (xxv, 834 p.)
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7.

電子ブック
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7. Machine learning algorithms and applications in engineering (: electronic bk)
edited by Prasenjit Chatterjee ... [et al.]
出版情報: EBSCOhost  1 online resource (xiv, 323 p.)
シリーズ名: Smart and intelligent computuing in engineering / series editor, Prasenjit Chatterjee ... [et al.]
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8.

電子ブック
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8. Practical MATLAB deep learning : a projects-based approach. 2nd ed (: electronic bk)
Michael Paluszek, Stephanie Thomas, Eric Ham
出版情報: ProQuest Ebook Central  1 online resource (xix, 329 p.)
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電子ブック
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9. Deep learning with Python : learn best practices of deep learning models with PyTorch. 2nd ed (: electronic bk)
Nikhil Ketkar, Jojo Moolayil
出版情報: ProQuest Ebook Central  1 online resource (xvii, 306 p.)
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10.

電子ブック
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10. Advanced deep learning for engineers and scientists : a practical approach (: electronic bk)
Kolla Bhanu Prakash ... [et al.], editors
出版情報: EBSCOhost  1 online resource (xvii, 285 p.)
シリーズ名: EAI/Springer innovations in communication and computing
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