Support vector machines for pattern classification:
Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Springer
2005
|
| Schriftenreihe: | Advances in pattern recognition
|
| Schlagworte: | |
| Zusammenfassung: | Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry. |
| Beschreibung: | XIV, 343 . graph. Darst. |
| ISBN: | 1852339292 |
Internformat
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| 520 | 3 | |a Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry. | |
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| discipline | Informatik |
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| id | DE-604.BV021273679 |
| illustrated | Illustrated |
| index_date | 2024-07-02T13:45:18Z |
| indexdate | 2024-07-09T20:34:25Z |
| institution | BVB |
| isbn | 1852339292 |
| language | English |
| lccn | 2005040265 |
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| physical | XIV, 343 . graph. Darst. |
| publishDate | 2005 |
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| publisher | Springer |
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| series2 | Advances in pattern recognition |
| spelling | Abe, Shigeo 1947- Verfasser (DE-588)122089545 aut Support vector machines for pattern classification Shigeo Abe London Springer 2005 XIV, 343 . graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advances in pattern recognition Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry. Apprentissage automatique Reconnaissance des formes (Informatique) Traitement de texte Text processing (Computer science) Pattern recognition systems Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 |
| spellingShingle | Abe, Shigeo 1947- Support vector machines for pattern classification Apprentissage automatique Reconnaissance des formes (Informatique) Traitement de texte Text processing (Computer science) Pattern recognition systems Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4040936-3 |
| title | Support vector machines for pattern classification |
| title_auth | Support vector machines for pattern classification |
| title_exact_search | Support vector machines for pattern classification |
| title_exact_search_txtP | Support vector machines for pattern classification |
| title_full | Support vector machines for pattern classification Shigeo Abe |
| title_fullStr | Support vector machines for pattern classification Shigeo Abe |
| title_full_unstemmed | Support vector machines for pattern classification Shigeo Abe |
| title_short | Support vector machines for pattern classification |
| title_sort | support vector machines for pattern classification |
| topic | Apprentissage automatique Reconnaissance des formes (Informatique) Traitement de texte Text processing (Computer science) Pattern recognition systems Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Apprentissage automatique Reconnaissance des formes (Informatique) Traitement de texte Text processing (Computer science) Pattern recognition systems Machine learning Maschinelles Lernen Mustererkennung |
| work_keys_str_mv | AT abeshigeo supportvectormachinesforpatternclassification |