Dataset shift in machine learning /:
This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions.
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2009.
|
| Schriftenreihe: | Neural information processing series.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions. |
| Beschreibung: | 1 online resource (xv, 229 pages) : illustrations |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliographie: | Includes bibliographical references (pages 207-218) and index. |
| ISBN: | 9780262255103 0262255103 1282240382 9781282240384 |
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| 520 | 8 | |a This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions. | |
| 505 | 0 | 0 | |g I. |t Introduction to dataset shift -- |g 1. |t When training and test sets are different: characterizing learning transfer / |r Amos Storkey -- |g 2. |t Projection and projectability / |r David Corfield -- |g II. |t Theoretical views on dataset and covariate shift -- |g 3. |t Binary classification under sample selection bias / |r Matthias Hein -- |g 4. |t On Bayesian transduction: implications for the covariate shift problem / |r Lars Kai Hansen -- |g 5. |t On the training/test distributions gap: a data representation learning framework / |r Shai Ben-David -- |g III. |t Algorithms for covariate shift -- |g 6. |t Geometry of covariate shift with applications to active learning / |r Takafumi Kanamori and Hidetoshi Shimodaira -- |g 7. |t A conditional expectation approach to model selection and active learning under covariate shift / |r Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller -- |g 8. |t Covariate shift by kernel mean matching / |r Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf -- |g 9. |t Discriminative learning under covariate shift with a single optimization problem / |r Steffen Bickel, Michael Bruckner and Tobias Scheffer -- |g 10. |t An adversarial view of covariate shift and a minimax approach / |r Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis -- |g IV. |t Discussion -- |g 11. |t Author comments / |r Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David. |
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| author_additional | Amos Storkey -- David Corfield -- Matthias Hein -- Lars Kai Hansen -- Shai Ben-David -- Takafumi Kanamori and Hidetoshi Shimodaira -- Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller -- Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf -- Steffen Bickel, Michael Bruckner and Tobias Scheffer -- Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis -- Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David. |
| author_facet | Quiñonero-Candela, Joaquin |
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| contents | Introduction to dataset shift -- When training and test sets are different: characterizing learning transfer / Projection and projectability / Theoretical views on dataset and covariate shift -- Binary classification under sample selection bias / On Bayesian transduction: implications for the covariate shift problem / On the training/test distributions gap: a data representation learning framework / Algorithms for covariate shift -- Geometry of covariate shift with applications to active learning / A conditional expectation approach to model selection and active learning under covariate shift / Covariate shift by kernel mean matching / Discriminative learning under covariate shift with a single optimization problem / An adversarial view of covariate shift and a minimax approach / Discussion -- Author comments / |
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| discipline | Informatik |
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| id | ZDB-4-EBA-ocn310915974 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:41Z |
| institution | BVB |
| isbn | 9780262255103 0262255103 1282240382 9781282240384 |
| language | English |
| oclc_num | 310915974 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xv, 229 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | MIT Press, |
| record_format | marc |
| series | Neural information processing series. |
| series2 | Neural information processing series |
| spelling | Dataset shift in machine learning / [edited by] Joaquin Quiñonero-Candela [and others]. Cambridge, Mass. : MIT Press, ©2009. 1 online resource (xv, 229 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Neural information processing series Includes bibliographical references (pages 207-218) and index. Print version record. Use copy Restrictions unspecified star MiAaHDL Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212 MiAaHDL digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL This work is an overview of recent efforts in the machine learning community to deal with dataset and covariate shift which occurs when test and training inputs and outputs have different distributions. I. Introduction to dataset shift -- 1. When training and test sets are different: characterizing learning transfer / Amos Storkey -- 2. Projection and projectability / David Corfield -- II. Theoretical views on dataset and covariate shift -- 3. Binary classification under sample selection bias / Matthias Hein -- 4. On Bayesian transduction: implications for the covariate shift problem / Lars Kai Hansen -- 5. On the training/test distributions gap: a data representation learning framework / Shai Ben-David -- III. Algorithms for covariate shift -- 6. Geometry of covariate shift with applications to active learning / Takafumi Kanamori and Hidetoshi Shimodaira -- 7. A conditional expectation approach to model selection and active learning under covariate shift / Masashi Sugiyama, Neil Rubens and Klaus-Robert Muller -- 8. Covariate shift by kernel mean matching / Arthur Grellon, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt and Bernhard Scholkopf -- 9. Discriminative learning under covariate shift with a single optimization problem / Steffen Bickel, Michael Bruckner and Tobias Scheffer -- 10. An adversarial view of covariate shift and a minimax approach / Amir Globerson, Choon Hui Teo, Alex Smola and Sam Roweis -- IV. Discussion -- 11. Author comments / Hidetoshi Shimodaira, Masashi Sugiyama, Amos Storkey, Arthur Gretton and Shai-Ben David. English. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Machine learning fast COMPUTER SCIENCE/Machine Learning & Neural Networks Quiñonero-Candela, Joaquin. has work: Dataset shift in machine learning (Text) https://id.oclc.org/worldcat/entity/E39PCGjJCq9gcWkW7tg6gB3TXm https://id.oclc.org/worldcat/ontology/hasWork Print version: Dataset shift in machine learning. Cambridge, Mass. : MIT Press, ©2009 9780262170055 0262170051 (DLC) 2008020394 (OCoLC)227205909 Neural information processing series. http://id.loc.gov/authorities/names/n00009051 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=259275 Volltext |
| spellingShingle | Dataset shift in machine learning / Neural information processing series. Introduction to dataset shift -- When training and test sets are different: characterizing learning transfer / Projection and projectability / Theoretical views on dataset and covariate shift -- Binary classification under sample selection bias / On Bayesian transduction: implications for the covariate shift problem / On the training/test distributions gap: a data representation learning framework / Algorithms for covariate shift -- Geometry of covariate shift with applications to active learning / A conditional expectation approach to model selection and active learning under covariate shift / Covariate shift by kernel mean matching / Discriminative learning under covariate shift with a single optimization problem / An adversarial view of covariate shift and a minimax approach / Discussion -- Author comments / Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Dataset shift in machine learning / |
| title_alt | Introduction to dataset shift -- When training and test sets are different: characterizing learning transfer / Projection and projectability / Theoretical views on dataset and covariate shift -- Binary classification under sample selection bias / On Bayesian transduction: implications for the covariate shift problem / On the training/test distributions gap: a data representation learning framework / Algorithms for covariate shift -- Geometry of covariate shift with applications to active learning / A conditional expectation approach to model selection and active learning under covariate shift / Covariate shift by kernel mean matching / Discriminative learning under covariate shift with a single optimization problem / An adversarial view of covariate shift and a minimax approach / Discussion -- Author comments / |
| title_auth | Dataset shift in machine learning / |
| title_exact_search | Dataset shift in machine learning / |
| title_full | Dataset shift in machine learning / [edited by] Joaquin Quiñonero-Candela [and others]. |
| title_fullStr | Dataset shift in machine learning / [edited by] Joaquin Quiñonero-Candela [and others]. |
| title_full_unstemmed | Dataset shift in machine learning / [edited by] Joaquin Quiñonero-Candela [and others]. |
| title_short | Dataset shift in machine learning / |
| title_sort | dataset shift in machine learning |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Machine learning fast |
| topic_facet | Machine learning. Apprentissage automatique. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=259275 |
| work_keys_str_mv | AT quinonerocandelajoaquin datasetshiftinmachinelearning |