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Density ratio estimation in machine learning /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Cambridge University Press,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Ia 4500 | ||
|---|---|---|---|
| 001 | EBSCO_ocn801405274 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 120723s2012 nyua ob 001 0 eng d | ||
| 040 | |a N$T |b eng |e pn |c N$T |d UIU |d COO |d YDXCP |d IUL |d OCLCQ |d OCLCF |d OCLCQ |d OCLCA |d OCLCQ |d BUF |d UAB |d OCLCQ |d OCLCO |d YDX |d INARC | ||
| 019 | |a 798344041 |a 1330602682 | ||
| 020 | |a 9781139233255 |q (electronic bk.) | ||
| 020 | |a 1139233254 |q (electronic bk.) | ||
| 020 | |a 9781139035613 |q (electronic bk.) | ||
| 020 | |a 1139035614 |q (electronic bk.) | ||
| 020 | |z 9780521190176 |q (hardback) | ||
| 020 | |z 0521190177 |q (hardback) | ||
| 024 | 8 | |a 7109893 | |
| 024 | 8 | |a 9786613719034 | |
| 035 | |a (OCoLC)801405274 |z (OCoLC)798344041 |z (OCoLC)1330602682 | ||
| 050 | 4 | |a QA276.8 |b .S84 2012eb | |
| 072 | 7 | |a COM |x 005030 |2 bisacsh | |
| 072 | 7 | |a COM |x 004000 |2 bisacsh | |
| 082 | 0 | 4 | |a 006.3/1 |2 23 |
| 084 | |a COM016000 |2 bisacsh | ||
| 049 | |a UAMI | ||
| 100 | 1 | |a Sugiyama, Masashi, |d 1974- | |
| 245 | 1 | 0 | |a Density ratio estimation in machine learning / |c Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori. |
| 260 | |a New York : |b Cambridge University Press, |c 2012. | ||
| 300 | |a 1 online resource (xii, 329 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 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 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"-- |c Provided by publisher. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Estimation theory. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Théorie de l'estimation. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a COMPUTERS |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Estimation theory. |2 fast |0 (OCoLC)fst00915531 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Suzuki, Taiji, |d 1981- | |
| 700 | 1 | |a Kanamori, Takafumi, |d 1972- | |
| 776 | 0 | 8 | |i Print version: |a Sugiyama, Masashi, 1974- |t Density ratio estimation in machine learning. |d New York : Cambridge University Press, 2012 |z 9780521190176 |w (DLC) 2011051726 |w (OCoLC)757931191 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=435286 |z Texto completo |
| 938 | |a EBSCOhost |b EBSC |n 435286 | ||
| 938 | |a YBP Library Services |b YANK |n 8923664 | ||
| 938 | |a YBP Library Services |b YANK |n 9003493 | ||
| 938 | |a YBP Library Services |b YANK |n 9445030 | ||
| 938 | |a Internet Archive |b INAR |n densityratioesti0000sugi | ||
| 938 | |a YBP Library Services |b YANK |n 7499418 | ||
| 994 | |a 92 |b IZTAP | ||