Support vector machines: optimization based theory, algorithms, and extensions
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press, Taylor & Francis Group
2013
|
| Schriftenreihe: | Chapman & Hall/CRC data mining and knowledge discovery series
|
| Schlagworte: | |
| Beschreibung: | "A Chapman & Hall book." Includes bibliographical references (p. 299-313) "Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfully applied in many fields such as text categorization, speech recognition, remote sensing image analysis, time series forecasting, information security and etc. SVMs, having their roots in Statistical Learning Theory (SLT) and optimization methods, become powerful tools to solve the problems of machine learning with finite training points and to overcome some traditional difficulties such as the "curse of dimensionality", "over-fitting" and etc. SVMs theoretical foundation and implementation techniques have been established and SVMs are gaining quick development and popularity due to their many attractive features: nice mathematical representations, geometrical explanations, good generalization abilities and promising empirical performance. Some SVM monographs, including more sophisticated ones such as Cristianini & Shawe-Taylor [39] and Scholkopf & Smola [124], have been published. We have published two books about SVMs in Science Press of China since 2004 [42, 43], which attracted widespread concerns and received favorable comments. After several years research and teaching, we decide to rewrite the books and add new research achievements. The starting point and focus of the book is optimization theory, which is different from other books on SVMs in this respect. Optimization is one of the pillars on which SVMs are built, so it makes a lot of sense to consider them from this point of view"-- |
| Beschreibung: | 1 Online-Ressource (xxvii, 313 p.) |
| ISBN: | 9781439857922 9781439857939 |
Internformat
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| 500 | |a "Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfully applied in many fields such as text categorization, speech recognition, remote sensing image analysis, time series forecasting, information security and etc. SVMs, having their roots in Statistical Learning Theory (SLT) and optimization methods, become powerful tools to solve the problems of machine learning with finite training points and to overcome some traditional difficulties such as the "curse of dimensionality", "over-fitting" and etc. SVMs theoretical foundation and implementation techniques have been established and SVMs are gaining quick development and popularity due to their many attractive features: nice mathematical representations, geometrical explanations, good generalization abilities and promising empirical performance. Some SVM monographs, including more sophisticated ones such as Cristianini & Shawe-Taylor [39] and Scholkopf & Smola [124], have been published. We have published two books about SVMs in Science Press of China since 2004 [42, 43], which attracted widespread concerns and received favorable comments. After several years research and teaching, we decide to rewrite the books and add new research achievements. The starting point and focus of the book is optimization theory, which is different from other books on SVMs in this respect. Optimization is one of the pillars on which SVMs are built, so it makes a lot of sense to consider them from this point of view"-- | ||
| 650 | 4 | |a Mathematical optimization | |
| 700 | 1 | |a Tian, Yingjie |e Verfasser |4 aut | |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Deng, Naiyang Tian, Yingjie Zhang, Chunhua |
| author_facet | Deng, Naiyang Tian, Yingjie Zhang, Chunhua |
| author_role | aut aut aut |
| author_sort | Deng, Naiyang |
| author_variant | n d nd y t yt c z cz |
| building | Verbundindex |
| bvnumber | BV041070013 |
| collection | ZDB-38-EBR |
| ctrlnum | (OCoLC)874335200 (DE-599)BVBBV041070013 |
| dewey-full | 519.6 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.6 |
| dewey-search | 519.6 |
| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV041070013 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:38:54Z |
| institution | BVB |
| isbn | 9781439857922 9781439857939 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026046966 |
| oclc_num | 874335200 |
| open_access_boolean | |
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| physical | 1 Online-Ressource (xxvii, 313 p.) |
| psigel | ZDB-38-EBR UER_PDA_EBR_Kauf |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Chapman & Hall/CRC data mining and knowledge discovery series |
| spelling | Deng, Naiyang Verfasser aut Support vector machines optimization based theory, algorithms, and extensions Naiyang Deng ; Yingjie Tian ; Chunhua Zhang Boca Raton CRC Press, Taylor & Francis Group 2013 1 Online-Ressource (xxvii, 313 p.) txt rdacontent c rdamedia cr rdacarrier Chapman & Hall/CRC data mining and knowledge discovery series "A Chapman & Hall book." Includes bibliographical references (p. 299-313) "Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in advance in their theoretical studies and implementations of algorithms. They have been successfully applied in many fields such as text categorization, speech recognition, remote sensing image analysis, time series forecasting, information security and etc. SVMs, having their roots in Statistical Learning Theory (SLT) and optimization methods, become powerful tools to solve the problems of machine learning with finite training points and to overcome some traditional difficulties such as the "curse of dimensionality", "over-fitting" and etc. SVMs theoretical foundation and implementation techniques have been established and SVMs are gaining quick development and popularity due to their many attractive features: nice mathematical representations, geometrical explanations, good generalization abilities and promising empirical performance. Some SVM monographs, including more sophisticated ones such as Cristianini & Shawe-Taylor [39] and Scholkopf & Smola [124], have been published. We have published two books about SVMs in Science Press of China since 2004 [42, 43], which attracted widespread concerns and received favorable comments. After several years research and teaching, we decide to rewrite the books and add new research achievements. The starting point and focus of the book is optimization theory, which is different from other books on SVMs in this respect. Optimization is one of the pillars on which SVMs are built, so it makes a lot of sense to consider them from this point of view"-- Mathematical optimization Tian, Yingjie Verfasser aut Zhang, Chunhua Verfasser aut |
| spellingShingle | Deng, Naiyang Tian, Yingjie Zhang, Chunhua Support vector machines optimization based theory, algorithms, and extensions Mathematical optimization |
| title | Support vector machines optimization based theory, algorithms, and extensions |
| title_auth | Support vector machines optimization based theory, algorithms, and extensions |
| title_exact_search | Support vector machines optimization based theory, algorithms, and extensions |
| title_full | Support vector machines optimization based theory, algorithms, and extensions Naiyang Deng ; Yingjie Tian ; Chunhua Zhang |
| title_fullStr | Support vector machines optimization based theory, algorithms, and extensions Naiyang Deng ; Yingjie Tian ; Chunhua Zhang |
| title_full_unstemmed | Support vector machines optimization based theory, algorithms, and extensions Naiyang Deng ; Yingjie Tian ; Chunhua Zhang |
| title_short | Support vector machines |
| title_sort | support vector machines optimization based theory algorithms and extensions |
| title_sub | optimization based theory, algorithms, and extensions |
| topic | Mathematical optimization |
| topic_facet | Mathematical optimization |
| work_keys_str_mv | AT dengnaiyang supportvectormachinesoptimizationbasedtheoryalgorithmsandextensions AT tianyingjie supportvectormachinesoptimizationbasedtheoryalgorithmsandextensions AT zhangchunhua supportvectormachinesoptimizationbasedtheoryalgorithmsandextensions |