Density ratio estimation in machine learning /
Sugiyama, Masashi, 1974-
Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori - Cambridge : Cambridge University Press, 2012 - xii, 329 p. : ill. ; 24 cm.
Includes bibliographical references and index
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
9780521190176 (hardback) 0521190177 (hardback)
Estimation theory
Machine learning
Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori - Cambridge : Cambridge University Press, 2012 - xii, 329 p. : ill. ; 24 cm.
Includes bibliographical references and index
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
9780521190176 (hardback) 0521190177 (hardback)
Estimation theory
Machine learning