Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) |
 enlarge | Author: Shigeo Abe
Publisher: Springer
Category: Book
List Price: $109.00
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Rating:  1 reviews
Sales Rank: 820785
Media: Hardcover
Edition: 1
Pages: 343
Number Of Items: 1
Shipping Weight (lbs): 1.4
Dimensions (in): 9.3 x 6.3 x 0.9
ISBN: 1852339292
Dewey Decimal Number: 005.52
EAN: 9781852339296
Publication Date: July 29, 2005
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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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Customer Reviews:
better ways to classify data? May 22, 2006
W Boudville (Terra, Sol 3)
0 out of 3 found this review helpful
When you have data that is present in some n-dimensional space, you often want to make clusters. The problem is that most methods have a subjective component. What is a cluster is sometimes a matter of definition, within a given method. Clusters can also be used to try to draw up regions of that n-dimensional space. This constitutes a classification of future data. Well, how to do so?
Abe explains an idea that has gained recognition recently. The concept of support vector machines. The label is perhaps a little clumsy. But Abe's book gives a good geometric understanding of current classification ideas and their limitations. And how these can be overcome using support vector machines.
Several variants are explored. Along with a tie-in to neural networks for training. The computations can be intensive for real data. But these days, that is less and less of a limitation.
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