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Sugiyama, Masashi,

Density ratio estimation in machine learning. - Delhi. Cambridge University Press. 2012 - xii, 329 p. : ill. ; 23 cm.

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-sampl...

"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 nonstationarity 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 includin..

ISBN: 9780521190176

Subjects--Topical Terms:
Estimation theory
Machine learning

Dewey Class. No.: 006.31 SUG-D