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Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
 
 
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Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) (Hardcover)

~ (Author) "Pattern classification is to classify some object into one of the given categories called classes..." (more)
Key Phrases: unclassifiable regions, fuzzy support vector machines, continuous decision functions, Introducing the Lagrange, Size Fig, Data Item One-against-all (more...)
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Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) + Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) + An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Editorial Reviews

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From the reviews:

"This broad and deep … book is organized around the highly significant concept of pattern recognition by support vector machines (SVMs). … The book is praxis and application oriented but with strong theoretical backing and support. Many … details are presented and discussed, thereby making the SVM both an easy-to-understand learning machine and a more likable data modeling (mining) tool. Shigeo Abe has produced the book that will become the standard … . I like it and therefore highly recommend this book … ." (Vojislav Kecman, SIAM Review, Vol. 48 (2), 2006)



Product Description

Support Vector Machines for Pattern Classification provides a comprehensive resource for the use of SVM?s in pattern classification. The subject area is particularly timely with research on kernel methods increasing rapidly; this book is unique in its focus on classification methods. The characteristic SVM?s are discussed: L1-SVMs and L2-SVMs, lease squares SVMs and linear programming SVMs from both a theoretical and an experimental viewpoint.

SVMs were originally formulated for two-class problems, and an extension to multiclass systems (which are essential for practical use) is not unique. However, in its discussion of several multiclass SVM architectures and the comparison of their performance using real world data, this book provides a unique perspective that researchers and students will find invaluable.

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More About the Author

Shigeo Abe
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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Inside This Book (learn more)
First Sentence:
Pattern classification is to classify some object into one of the given categories called classes. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
unclassifiable regions, fuzzy support vector machines, continuous decision functions, water purification data, support vector regressor, discrete decision functions, blood cell data, conventional support vector machines, correcting classifiers, linear programming support vector machines, kernel least squares, thyroid data sets, highest generalization ability, polynomial kernel with degree, training speedup, training time comparison, bounded support vectors, violating variables, unbounded support vector, positive semidefinite kernels, pairwise classification, working set size, training datum, squares support vector machines, following dual problem
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Introducing the Lagrange, Size Fig, Data Item One-against-all, High-Dimensional Space, Steepest Ascent Methods
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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