Conformal prediction for reliable machine learning: theory, adaptations, and applications
"Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensio...
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| Weitere Verfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam Boston
Morgan Kaufmann
© 2014
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| Schlagworte: | |
| Online-Zugang: | FLA01 Volltext |
| Zusammenfassung: | "Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of confidence in the predicted labels of new, unclassifed examples. Confidence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 online resource |
| ISBN: | 9780124017153 0124017150 1306697484 9781306697484 |
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| 245 | 1 | 0 | |a Conformal prediction for reliable machine learning |b theory, adaptations, and applications |c [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV046126570 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:35:54Z |
| institution | BVB |
| isbn | 9780124017153 0124017150 1306697484 9781306697484 |
| language | English |
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| publisher | Morgan Kaufmann |
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| spelling | Conformal prediction for reliable machine learning theory, adaptations, and applications [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk Amsterdam Boston Morgan Kaufmann © 2014 1 online resource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index "Traditional, low-dimensional, small scale data have been successfully dealt with using conventional software engineering and classical statistical methods, such as discriminant analysis, neural networks, genetic algorithms and others. But the change of scale in data collection and the dimensionality of modern data sets has profound implications on the type of analysis that can be done. Recently several kernel-based machine learning algorithms have been developed for dealing with high-dimensional problems, where a large number of features could cause a combinatorial explosion. These methods are quickly gaining popularity, and it is widely believed that they will help to meet the challenge of analysing very large data sets. Learning machines often perform well in a wide range of applications and have nice theoretical properties without requiring any parametric statistical assumption about the source of data (unlike traditional statistical techniques). However, a typical drawback of many machine learning algorithms is that they usually do not provide any useful measure of confidence in the predicted labels of new, unclassifed examples. Confidence estimation is a well-studied area of both parametric and non-parametric statistics; however, usually only low-dimensional problems are considered"-- COMPUTERS / General bisacsh Machine learning fast Aprenentatge automàtic Machine learning Balasubramanian, Vineeth edt Ho, Shen-Shyang edt Vovk, Vladimir 1960- edt Erscheint auch als Druck-Ausgabe 0123985374 Erscheint auch als Druck-Ausgabe 9780123985378 http://www.sciencedirect.com/science/book/9780123985378 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Conformal prediction for reliable machine learning theory, adaptations, and applications COMPUTERS / General bisacsh Machine learning fast Aprenentatge automàtic Machine learning |
| title | Conformal prediction for reliable machine learning theory, adaptations, and applications |
| title_auth | Conformal prediction for reliable machine learning theory, adaptations, and applications |
| title_exact_search | Conformal prediction for reliable machine learning theory, adaptations, and applications |
| title_full | Conformal prediction for reliable machine learning theory, adaptations, and applications [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk |
| title_fullStr | Conformal prediction for reliable machine learning theory, adaptations, and applications [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk |
| title_full_unstemmed | Conformal prediction for reliable machine learning theory, adaptations, and applications [edited by] Vineeth Balasubramanian, Shen-Shyang Ho, Vladimir Vovk |
| title_short | Conformal prediction for reliable machine learning |
| title_sort | conformal prediction for reliable machine learning theory adaptations and applications |
| title_sub | theory, adaptations, and applications |
| topic | COMPUTERS / General bisacsh Machine learning fast Aprenentatge automàtic Machine learning |
| topic_facet | COMPUTERS / General Machine learning Aprenentatge automàtic |
| url | http://www.sciencedirect.com/science/book/9780123985378 |
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