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Record Nr. |
UNINA9910789928103321 |
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Autore |
Sugiyama Masashi <1974-> |
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Titolo |
Machine learning in non-stationary environments : introduction to covariate shift adaptation / / Masashi Sugiyama and Motoaki Kawanabe |
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Pubbl/distr/stampa |
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Cambridge, Mass., : MIT Press, ©2012 |
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ISBN |
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0-262-30043-5 |
1-280-49922-2 |
9786613594457 |
0-262-30122-9 |
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Descrizione fisica |
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1 online resource (279 p.) |
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Collana |
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Adaptive computation and machine learning |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 |
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