Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

Density Ratio Estimation in Machine Learning

• Gebundenes Buch

Produktbeschreibung

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

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 342
• Erscheinungstermin: 1. Februar 2012
• Englisch
• Abmessung: 240mm x 161mm x 23mm
• Gewicht: 680g
• ISBN-13: 9780521190176
• ISBN-10: 0521190177
• Artikelnr.: 34743622

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.