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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Cambridge University Press,
2012.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 1 online resource (xii, 329 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781139233255 1139233254 9781139035613 1139035614 9781280877728 1280877723 |
Internformat
MARC
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| 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. | |
| 650 | 0 | |a Estimation theory. |0 http://id.loc.gov/authorities/subjects/sh85044957 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 6 | |a Théorie de l'estimation. | |
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-ocn801405274 |
|---|---|
| _version_ | 1816882201154813952 |
| adam_text | |
| any_adam_object | |
| author | Sugiyama, Masashi, 1974- |
| author2 | Suzuki, Taiji, 1981- Kanamori, Takafumi, 1972- |
| author2_role | |
| author2_variant | t s ts t k tk |
| author_GND | http://id.loc.gov/authorities/names/n2011062656 http://id.loc.gov/authorities/names/n2012006280 |
| author_facet | Sugiyama, Masashi, 1974- Suzuki, Taiji, 1981- Kanamori, Takafumi, 1972- |
| author_role | |
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| 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. |
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| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:24:51Z |
| institution | BVB |
| isbn | 9781139233255 1139233254 9781139035613 1139035614 9781280877728 1280877723 |
| language | English |
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| publisher | Cambridge University Press, |
| record_format | marc |
| spelling | Sugiyama, Masashi, 1974- https://id.oclc.org/worldcat/entity/E39PBJgCGQdKGGMBPRGxy6RqcP http://id.loc.gov/authorities/names/n2011062656 Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori. New York : Cambridge University Press, 2012. 1 online resource (xii, 329 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. 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. "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"-- Provided by publisher. Print version record. Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Théorie de l'estimation. Apprentissage automatique. COMPUTERS Computer Vision & Pattern Recognition. bisacsh COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Estimation theory. fast (OCoLC)fst00915531 Machine learning. fast (OCoLC)fst01004795 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf Suzuki, Taiji, 1981- https://id.oclc.org/worldcat/entity/E39PCjv7Mw8JtMcVR9mq9vPPcP http://id.loc.gov/authorities/names/n2012006280 Kanamori, Takafumi, 1972- https://id.oclc.org/worldcat/entity/E39PCjJ73TKQcKkHmFg4J3Y9Dq has work: Density ratio estimation in machine learning (Text) https://id.oclc.org/worldcat/entity/E39PCGXCwBCwtdKCWQQyc4P4YK https://id.oclc.org/worldcat/ontology/hasWork Print version: Sugiyama, Masashi, 1974- Density ratio estimation in machine learning. New York : Cambridge University Press, 2012 9780521190176 (DLC) 2011051726 (OCoLC)757931191 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=435286 Volltext |
| spellingShingle | Sugiyama, Masashi, 1974- Density ratio estimation in machine learning / 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. Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Théorie de l'estimation. Apprentissage automatique. COMPUTERS Computer Vision & Pattern Recognition. bisacsh COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Estimation theory. fast (OCoLC)fst00915531 Machine learning. fast (OCoLC)fst01004795 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85044957 http://id.loc.gov/authorities/subjects/sh85079324 (OCoLC)fst00915531 (OCoLC)fst01004795 https://id.nlm.nih.gov/mesh/D000069550 http://id.loc.gov/authorities/genreForms/gf2014026039 |
| title | Density ratio estimation in machine learning / |
| title_auth | Density ratio estimation in machine learning / |
| title_exact_search | Density ratio estimation in machine learning / |
| title_full | Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori. |
| title_fullStr | Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori. |
| title_full_unstemmed | Density ratio estimation in machine learning / Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori. |
| title_short | Density ratio estimation in machine learning / |
| title_sort | density ratio estimation in machine learning |
| topic | Estimation theory. http://id.loc.gov/authorities/subjects/sh85044957 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Théorie de l'estimation. Apprentissage automatique. COMPUTERS Computer Vision & Pattern Recognition. bisacsh COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Estimation theory. fast (OCoLC)fst00915531 Machine learning. fast (OCoLC)fst01004795 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 |
| topic_facet | Estimation theory. Machine learning. Théorie de l'estimation. Apprentissage automatique. COMPUTERS Computer Vision & Pattern Recognition. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Machine Learning dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=435286 |
| work_keys_str_mv | AT sugiyamamasashi densityratioestimationinmachinelearning AT suzukitaiji densityratioestimationinmachinelearning AT kanamoritakafumi densityratioestimationinmachinelearning |