Support Vector Machines for Pattern Classification
Abe, Shigeo.
Support Vector Machines for Pattern Classification [electronic resource] / by Shigeo Abe. - XIV, 344 p. 110 illus. online resource. - Advances in Pattern Recognition . - Advances in Pattern Recognition .
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].
9781846282195
10.1007/1-84628-219-5 doi
Computer science.
Artificial intelligence.
Text processing (Computer science.
Optical pattern recognition.
Computer Science.
Pattern Recognition.
Document Preparation and Text Processing.
Artificial Intelligence (incl. Robotics).
Control Engineering.
Q337.5 TK7882.P3
006.4
Support Vector Machines for Pattern Classification [electronic resource] / by Shigeo Abe. - XIV, 344 p. 110 illus. online resource. - Advances in Pattern Recognition . - Advances in Pattern Recognition .
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].
9781846282195
10.1007/1-84628-219-5 doi
Computer science.
Artificial intelligence.
Text processing (Computer science.
Optical pattern recognition.
Computer Science.
Pattern Recognition.
Document Preparation and Text Processing.
Artificial Intelligence (incl. Robotics).
Control Engineering.
Q337.5 TK7882.P3
006.4