Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

Density Ratio Estimation in Machine Learning

• Broschiertes Buch

Produktbeschreibung

This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 342
• Erscheinungstermin: 1. Februar 2018
• Englisch
• Abmessung: 234mm x 156mm x 19mm
• Gewicht: 521g
• ISBN-13: 9781108461733
• ISBN-10: 1108461735
• Artikelnr.: 52159779

Autorenporträt

Masashi Sugiyama is an Associate Professor in the Department of Computer Science at the Tokyo Institute of Technology.

Inhaltsangabe

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.