Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki and Takafumi Kanamori.
Material type: TextPublication details: New York Cambridge University Press 2012Description: 329 pISBN:- 9780521190176 (hardback)
- 0521190177 (hardback)
- 006.31 SUG-M 23
- QA276.8 .S84 2012
- COM016000
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | BITS Pilani Hyderabad | 003-007 | General Stack (For lending) | 006.31 SUG-M (Browse shelf(Opens below)) | Available | 26584 |
"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 including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting as well as describing how these can be applied to machine learning. The book also provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning"--
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