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Machine learning in non-stationary environments : introduction to covariate shift adaptation / / Masashi Sugiyama and Motoaki Kawanabe
| Machine learning in non-stationary environments : introduction to covariate shift adaptation / / Masashi Sugiyama and Motoaki Kawanabe |
| Autore | Sugiyama Masashi <1974-> |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2012 |
| Descrizione fisica | 1 online resource (279 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | KawanabeMotoaki |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Machine learning |
| Soggetto non controllato |
COMPUTER SCIENCE/Machine Learning & Neural Networks
COMPUTER SCIENCE/General COMPUTER SCIENCE/Artificial Intelligence |
| ISBN |
0-262-30043-5
1-280-49922-2 9786613594457 0-262-30122-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Foreword; Preface; I INTRODUCTION; 1 Introduction and Problem Formulation; 1.1 Machine Learning under Covariate Shift; 1.2 Quick Tour of Covariate Shift Adaptation; 1.3 Problem Formulation; 1.4 Structure of This Book; II LEARNING UNDER COVARIATE SHIFT; 2 Function Approximation; 2.1 Importance-Weighting Techniques for Covariate Shift Adaptation; 2.2 Examples of Importance-Weighted Regression Methods; 2.3 Examples of Importance-Weighted Classification Methods; 2.4 Numerical Examples; 2.5 Summary and Discussion; 3 Model Selection; 3.1 Importance-Weighted Akaike Information Criterion
3.2 Importance-Weighted Subspace Information Criterion3.3 Importance-Weighted Cross-Validation; 3.4 Numerical Examples; 3.5 Summary and Discussion; 4 Importance Estimation; 4.1 Kernel Density Estimation; 4.2 Kernel Mean Matching; 4.3 Logistic Regression; 4.4 Kullback-Leibler Importance Estimation Procedure; 4.5 Least-Squares Importance Fitting; 4.6 Unconstrained Least-Squares Importance Fitting; 4.7 Numerical Examples; 4.8 Experimental Comparison; 4.9 Summary; 5 Direct Density-Ratio Estimation with Dimensionality Reduction; 5.1 Density Difference in Hetero-Distributional Subspace 5.2 Characterization of Hetero-Distributional Subspace5.3 Identifying Hetero-Distributional Subspace by Supervised Dimensionality Reduction; 5.4 Using LFDA for Finding Hetero-Distributional Subspace; 5.5 Density-Ratio Estimation in the Hetero-Distributional Subspace; 5.6 Numerical Examples; 5.7 Summary; 6 Relation to Sample Selection Bias; 6.1 Heckman's Sample Selection Model; 6.2 Distributional Change and Sample Selection Bias; 6.3 The Two-Step Algorithm; 6.4 Relation to Covariate Shift Approach; 7 Applications of Covariate Shift Adaptation; 7.1 Brain-Computer Interface 7.2 Speaker Identification7.3 Natural Language Processing; 7.4 Perceived Age Prediction from Face Images; 7.5 Human Activity Recognition from Accelerometric Data; 7.6 Sample Reuse in Reinforcement Learning; III LEARNING CAUSING COVARIATE SHIFT; 8 Active Learning; 8.1 Preliminaries; 8.2 Population-Based Active Learning Methods; 8.3 Numerical Examples of Population-Based Active Learning Methods; 8.4 Pool-Based Active Learning Methods; 8.5 Numerical Examples of Pool-Based Active Learning Methods; 8.6 Summary and Discussion; 9 Active Learning with Model Selection 9.1 Direct Approach and the Active Learning/Model Selection Dilemma9.2 Sequential Approach; 9.3 Batch Approach; 9.4 Ensemble Active Learning; 9.5 Numerical Examples; 9.6 Summary and Discussion; 10 Applications of Active Learning; 10.1 Design of Efficient Exploration Strategies in Reinforcement Learning; 10.2 Wafer Alignment in Semiconductor Exposure Apparatus; IV CONCLUSIONS; 11 Conclusions and Future Prospects; 11.1 Conclusions; 11.2 Future Prospects; Appendix: List of Symbols and Abbreviations; Bibliography; Index |
| Record Nr. | UNINA-9910789928103321 |
Sugiyama Masashi <1974->
|
||
| Cambridge, Mass., : MIT Press, ©2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning in non-stationary environments : introduction to covariate shift adaptation / / Masashi Sugiyama and Motoaki Kawanabe
| Machine learning in non-stationary environments : introduction to covariate shift adaptation / / Masashi Sugiyama and Motoaki Kawanabe |
| Autore | Sugiyama Masashi <1974-> |
| Pubbl/distr/stampa | Cambridge, Mass., : MIT Press, ©2012 |
| Descrizione fisica | 1 online resource (279 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | KawanabeMotoaki |
| Collana | Adaptive computation and machine learning |
| Soggetto topico | Machine learning |
| Soggetto non controllato |
COMPUTER SCIENCE/Machine Learning & Neural Networks
COMPUTER SCIENCE/General COMPUTER SCIENCE/Artificial Intelligence |
| ISBN |
0-262-30043-5
1-280-49922-2 9786613594457 0-262-30122-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Foreword; Preface; I INTRODUCTION; 1 Introduction and Problem Formulation; 1.1 Machine Learning under Covariate Shift; 1.2 Quick Tour of Covariate Shift Adaptation; 1.3 Problem Formulation; 1.4 Structure of This Book; II LEARNING UNDER COVARIATE SHIFT; 2 Function Approximation; 2.1 Importance-Weighting Techniques for Covariate Shift Adaptation; 2.2 Examples of Importance-Weighted Regression Methods; 2.3 Examples of Importance-Weighted Classification Methods; 2.4 Numerical Examples; 2.5 Summary and Discussion; 3 Model Selection; 3.1 Importance-Weighted Akaike Information Criterion
3.2 Importance-Weighted Subspace Information Criterion3.3 Importance-Weighted Cross-Validation; 3.4 Numerical Examples; 3.5 Summary and Discussion; 4 Importance Estimation; 4.1 Kernel Density Estimation; 4.2 Kernel Mean Matching; 4.3 Logistic Regression; 4.4 Kullback-Leibler Importance Estimation Procedure; 4.5 Least-Squares Importance Fitting; 4.6 Unconstrained Least-Squares Importance Fitting; 4.7 Numerical Examples; 4.8 Experimental Comparison; 4.9 Summary; 5 Direct Density-Ratio Estimation with Dimensionality Reduction; 5.1 Density Difference in Hetero-Distributional Subspace 5.2 Characterization of Hetero-Distributional Subspace5.3 Identifying Hetero-Distributional Subspace by Supervised Dimensionality Reduction; 5.4 Using LFDA for Finding Hetero-Distributional Subspace; 5.5 Density-Ratio Estimation in the Hetero-Distributional Subspace; 5.6 Numerical Examples; 5.7 Summary; 6 Relation to Sample Selection Bias; 6.1 Heckman's Sample Selection Model; 6.2 Distributional Change and Sample Selection Bias; 6.3 The Two-Step Algorithm; 6.4 Relation to Covariate Shift Approach; 7 Applications of Covariate Shift Adaptation; 7.1 Brain-Computer Interface 7.2 Speaker Identification7.3 Natural Language Processing; 7.4 Perceived Age Prediction from Face Images; 7.5 Human Activity Recognition from Accelerometric Data; 7.6 Sample Reuse in Reinforcement Learning; III LEARNING CAUSING COVARIATE SHIFT; 8 Active Learning; 8.1 Preliminaries; 8.2 Population-Based Active Learning Methods; 8.3 Numerical Examples of Population-Based Active Learning Methods; 8.4 Pool-Based Active Learning Methods; 8.5 Numerical Examples of Pool-Based Active Learning Methods; 8.6 Summary and Discussion; 9 Active Learning with Model Selection 9.1 Direct Approach and the Active Learning/Model Selection Dilemma9.2 Sequential Approach; 9.3 Batch Approach; 9.4 Ensemble Active Learning; 9.5 Numerical Examples; 9.6 Summary and Discussion; 10 Applications of Active Learning; 10.1 Design of Efficient Exploration Strategies in Reinforcement Learning; 10.2 Wafer Alignment in Semiconductor Exposure Apparatus; IV CONCLUSIONS; 11 Conclusions and Future Prospects; 11.1 Conclusions; 11.2 Future Prospects; Appendix: List of Symbols and Abbreviations; Bibliography; Index |
| Record Nr. | UNINA-9910825516303321 |
|
Sugiyama Masashi <1974->
|
||
| Cambridge, Mass., : MIT Press, ©2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||