Support vector machines optimization based theory, algorithms, and extensions
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 appl...
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| Other Authors: | , |
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton
CRC Press, Taylor & Francis Group
2013
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| Series: | Chapman & Hall/CRC data mining and knowledge discovery series
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| Subjects: | |
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| 010 | |a 2012-033209 | ||
| 020 | |a 9781439857922 (hardback) | ||
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| 100 | 1 | |a Deng, Naiyang. | |
| 245 | 1 | 0 | |a Support vector machines |b optimization based theory, algorithms, and extensions |c Naiyang Deng, Yingjie Tian, Chunhua Zhang. |
| 260 | |a Boca Raton |b CRC Press, Taylor & Francis Group |c 2013 | ||
| 300 | |a xxvii, 335 pages |b illustrations |c 24 cm. | ||
| 490 | 1 | |a Chapman & Hall/CRC data mining and knowledge discovery series | |
| 504 | |a Includes bibliographical references (pages 299-313) and index. | ||
| 520 | |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. | ||
| 592 | |a 9544 |b 20/12/2013 |c RM 277.95 |h RIMA | ||
| 650 | 0 | |a Mathematical optimization. | |
| 700 | 1 | |a Tian, Yingjie, |d 1973- | |
| 700 | 1 | |a Zhang, Chunhua, |d 1978- | |
| 830 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series | |
| 999 | |a vtls000050083 |c 49363 |d 49363 | ||