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Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition)

Shigeo Abe

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9781852339296: Support Vector Machines for Pattern Classification (Advances in Computer Vision and 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.

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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.

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, 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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Book Description Springer, 2005. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Introduction.- Two-class Support Vector Machines.- Multiclass Support Vector Machines.- Variants of Support Vector Machines.- Training Methods.- Feature Selection and Extraction.- Clustering.- Kernel-Based Methods.- Maximum Margin Multilayer Neural Networks.- Maximum Margin Fuzzy Classifiers.- Function Approximation.- Conventional Classifiers.- Matrices. Bookseller Inventory # ABE_book_new_1852339292

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