Density Ratio Estimation in Machine Learning

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Density Ratio Estimation in Machine Learning

Sugiyama, Masashi/ Suzuki, Taiji/ Kanamori, Takafumi

ウェブストア価格 ¥36,241（本体¥32,947）
Cambridge University Press（2012/02発売）
外貨定価 US$ 165.00
ポイント 329pt

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製本 Hardcover:ハードカバー版／ページ数 342 p.
言語 ENG
商品コード 9780521190176
DDC分類 006.31

基本説明

Introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting as well as describing how these can be applied to machine learning.

Full Description

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.

Part I. Density Ratio Approach to Machine Learning: 1. Introduction; Part II. Methods of Density Ratio Estimation: 2. Density estimation; 3. Moment matching; 4. Probabilistic classification; 5. Density fitting; 6. Density-ratio fitting; 7. Unified framework; 8. Direct density-ratio estimation with dimensionality reduction; Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling; 10. Distribution comparison; 11. Mutual information estimation; 12. Conditional probability estimation; Part IV. Theoretical Analysis of Density Ratio Estimation: 13. Parametric convergence analysis; 14. Non-parametric convergence analysis; 15. Parametric two-sample test; 16. Non-parametric numerical stability analysis; Part V. Conclusions: 17. Conclusions and future directions.