Support Vector Machines for Pattern Classification [electronic resource] / by Shigeo Abe.
Material type: TextSeries: Advances in Pattern RecognitionPublisher: London : Springer London, 2005Description: XIV, 344 p. 110 illus. online resourceContent type:- text
- computer
- online resource
- 9781846282195
- 006.4 23
- Q337.5
- TK7882.P3
Item type | Current library | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|
E-Book | Central Library | Available | E-40348 |
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.
I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research e?orts from developing fuzzy classi?ers with high generalization ability to developing support vector machine–based classi?ers. This book focuses on the application of support vector machines to p- tern classi?cation. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines. To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257].
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