Density Ratio Estimation in Machine Learning
Autor Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamorien Limba Engleză Hardback – 19 feb 2012
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Specificații
ISBN-13: 9780521190176
ISBN-10: 0521190177
Pagini: 342
Ilustrații: 79 b/w illus. 18 tables
Dimensiuni: 157 x 234 x 23 mm
Greutate: 0.6 kg
Ediția:New.
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:New York, United States
ISBN-10: 0521190177
Pagini: 342
Ilustrații: 79 b/w illus. 18 tables
Dimensiuni: 157 x 234 x 23 mm
Greutate: 0.6 kg
Ediția:New.
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:New York, United States
Cuprins
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.
Recenzii
'There is no doubt that this book will change the way people think about machine learning and stimulate many new directions for research.' Thomas G. Dietterich, from the Foreword
Notă biografică
Descriere
This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.