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100			_aSugiyama, Masashi, _919128
245		0	_aDensity ratio estimation in machine learning.
260			_aDelhi. _bCambridge University Press. _c2012
300			_axii, 329 p. : ill. ; 23 cm.
505			_aPart 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-sampl...
520			_a"Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as nonstationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators includin..
650			_aEstimation theory _988369
650			_a Machine learning _988370
700			_a Suzuki, Taiji _988371
700			_aKanamori, Takafumi _988372
942			_cBK
999			_c50574 _d50574