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Density Ratio Estimation in Machine Learning

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Table of 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.

About the Author

Masashi Sugiyama is an Associate Professor in the Department of Computer Science at the Tokyo Institute of Technology. Taiji Suzuki is an Assistant Professor in the Department of Mathematical Informatics at the University of Tokyo, Japan. Takafumi Kanamori is an Associate Professor in the Department of Computer Science and Mathematical Informatics at Nagoya University, Japan.


'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

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