Density ratio estimation in machine learning. (Record no. 50574)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260713105538.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
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| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780521190176 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 SUG-D |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Sugiyama, Masashi, |
| 245 #0 - TITLE STATEMENT | |
| Title | Density ratio estimation in machine learning. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Delhi. |
| Name of publisher | Cambridge University Press. |
| Year of publication | 2012 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xii, 329 p. : ill. ; 23 cm. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Part I. Density-Ratio Approach to Machine Learning: 1. Introduction -- Part II. Methods of Density-Ratio Estimation: 2. Density estimation; 3.<br/>Moment matching; 4. Probabilistic classification; 5. Density fitting; 6. Density-ratio fitting; 7. Unified framework; 8. Direct density-ratio<br/>estimation with dimensionality reduction -- Part III. Applications of Density Ratios in Machine Learning: 9. Importance sampling; 10.<br/>Distribution comparison; 11. Mutual information estimation; 12. Conditional probability estimation -- Part IV. Theoretical Analysis of<br/>Density-Ratio Estimation: 13. Parametric convergence analysis; 14. Non-parametric convergence analysis; 15. Parametric two-sampl... |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of<br/>systems that learn. This book introduces theories, methods, and applications of density ratio estimation, which is a newly emerging paradigm<br/>in the machine learning community. Various machine learning problems such as nonstationarity adaptation, outlier detection, dimensionality<br/>reduction, independent component analysis, clustering, classification, and conditional density estimation can be systematically solved via the<br/>estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators includin.. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Estimation theory |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Suzuki, Taiji |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Kanamori, Takafumi |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books and Monographs |
| Full call number | Accession Number | Koha item type | Lost status | Damaged status | Permanent Location | Current Location | Shelving location | Date acquired |
|---|---|---|---|---|---|---|---|---|
| 006.31 SUG-D | 91422 | Books and Monographs | Central Library, NIT Jalandhar | Central Library, NIT Jalandhar | General Stacks | 20.07.2012 |