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130626 ||| eng |
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|a 9781849960984
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|a Abe, Shigeo
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| 245 |
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|a Support Vector Machines for Pattern Classification
|h Elektronische Ressource
|c by Shigeo Abe
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| 250 |
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|a 2nd ed. 2010
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| 260 |
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|a London
|b Springer London
|c 2010, 2010
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| 300 |
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|a XX, 473 p. 114 illus
|b online resource
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| 505 |
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|a Two-Class Support Vector Machines -- Multiclass Support Vector Machines -- Variants of Support Vector Machines -- Training Methods -- Kernel-Based Methods Kernel@Kernel-based method -- Feature Selection and Extraction -- Clustering -- Maximum-Margin Multilayer Neural Networks -- Maximum-Margin Fuzzy Classifiers -- Function Approximation
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Control and Systems Theory
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| 653 |
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|a Control engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Robotics
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Automation
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| 653 |
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|a Natural language processing (Computer science)
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| 653 |
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|a Pattern recognition systems
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Advances in Computer Vision and Pattern Recognition
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| 028 |
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|a 10.1007/978-1-84996-098-4
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| 856 |
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|u https://doi.org/10.1007/978-1-84996-098-4?nosfx=y
|x Verlag
|3 Volltext
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|a 629.8312
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|a 003
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| 520 |
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|a Topics and Features: Clarifies the characteristics of two-class SVMs through extensive analysis Discusses kernel methods for improving the generalization ability of conventional neural networks and fuzzy systems Contains ample illustrations, examples and computer experiments to help readers understand the concepts and their usefulness Includes performance evaluation using publicly available two-class data sets, microarray sets, multiclass data sets, and regression data sets (NEW) Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation (NEW) Covers sparse SVMs, an approach to learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning (NEW) Explores incremental training based batch training and active-set training methods,
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|a Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction methods. Providing a unique perspective on the state of the art in SVMs, with a particular focus on classification, this thoroughly updated new edition includes a more rigorous performance comparison of classifiers and regressors. In addition to presenting various useful architectures for multiclass classification and function approximation problems, the book now also investigates evaluation criteria for classifiers and regressors.
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| 520 |
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|a together with decomposition techniques for linear programming SVMs (NEW) Provides a discussion on variable selection for support vector regressors (NEW) An essential guide on the use of SVMs in pattern classification, this comprehensive resource will be of interest to researchers and postgraduate students, as well as professional developers. Dr. Shigeo Abe is a Professor at Kobe University, Graduate School of Engineering. He is the author of the Springer titles Neural Networks and Fuzzy Systems and Pattern Classification: Neuro-fuzzy Methods and Their Comparison
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