Advances in large margin classifiers /:
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classi...
Gespeichert in:
| Weitere Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2000.
|
| Schriftenreihe: | Neural information processing series.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba. |
| Beschreibung: | 1 online resource (vi, 412 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 389-407) and index. |
| ISBN: | 9780262283977 0262283972 1423729544 9781423729549 0262292408 9780262292405 0262194481 9780262194488 |
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| indexdate | 2024-11-27T13:15:47Z |
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| series | Neural information processing series. |
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| spelling | Advances in large margin classifiers / edited by Alexander J. Smola [and others]. Cambridge, Mass. : MIT Press, ©2000. 1 online resource (vi, 412 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rdaft Advances in neural information processing systems [i.e. Neural information processing series] Includes bibliographical references (pages 389-407) and index. Print version record. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba. English. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computer algorithms fast Algorithms fast Kernel functions fast Machine learning fast COMPUTER SCIENCE/General Smola, Alexander J. Print version: Advances in large margin classifiers. Cambridge, Mass. : MIT Press, ©2000 0262194481 (DLC) 00027641 (OCoLC)43561865 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=138583 Volltext |
| spellingShingle | Advances in large margin classifiers / Neural information processing series. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computer algorithms fast Algorithms fast Kernel functions fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85003487 http://id.loc.gov/authorities/subjects/sh85072061 http://id.loc.gov/authorities/subjects/sh91000149 https://id.nlm.nih.gov/mesh/D000465 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Advances in large margin classifiers / |
| title_auth | Advances in large margin classifiers / |
| title_exact_search | Advances in large margin classifiers / |
| title_full | Advances in large margin classifiers / edited by Alexander J. Smola [and others]. |
| title_fullStr | Advances in large margin classifiers / edited by Alexander J. Smola [and others]. |
| title_full_unstemmed | Advances in large margin classifiers / edited by Alexander J. Smola [and others]. |
| title_short | Advances in large margin classifiers / |
| title_sort | advances in large margin classifiers |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Kernel functions. http://id.loc.gov/authorities/subjects/sh85072061 Computer algorithms. http://id.loc.gov/authorities/subjects/sh91000149 Algorithms https://id.nlm.nih.gov/mesh/D000465 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Computer algorithms fast Algorithms fast Kernel functions fast Machine learning fast |
| topic_facet | Machine learning. Algorithms. Kernel functions. Computer algorithms. Algorithms Machine Learning Apprentissage automatique. Algorithmes. Noyaux (Mathématiques) algorithms. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Computer algorithms Kernel functions Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=138583 |
| work_keys_str_mv | AT smolaalexanderj advancesinlargemarginclassifiers |