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Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

By: Contributor(s): Material type: TextTextPublication details: Cambridge : Cambridge University Press, 2012Description: xii, 329 p. : ill. ; 24 cmISBN:
  • 9780521190176 (hardback)
  • 0521190177 (hardback)
Subject(s):
Contents:
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
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Open Shelf Open Shelf UMPLIB PEKAN QA276.8 .S84 2012 (Browse shelf(Opens below)) 1 Available 0000079687

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

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