Sugiyama, Masashi 1974- creator Suzuki, Taiji 1981- Kanamori, Takafumi 1971- text enk Cambridge Cambridge University Press 2012 monographic eng

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xii, 329 p. : ill. ; 24 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-sample test; 16. Non-parametric numerical stability analysis -- Part V. Conclusions: 17. Conclusions and future directions specialized Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori Includes bibliographical references and index Estimation theory Machine learning 9780521190176 (hardback) 0521190177 (hardback) UMP 130605 20251125093717.0 vtls000070612