TY  - BOOK
AU  - Abe,Shigeo
ED  - SpringerLink (Online service)
TI  - Support Vector Machines for Pattern Classification
T2  - Advances in Pattern Recognition
SN  - 9781846282195
AV  - Q337.5 
U1  - 006.4 23
PY  - 2005///
CY  - London
PB  - Springer London
KW  - Computer science
KW  - Artificial intelligence
KW  - Text processing (Computer science
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Pattern Recognition
KW  - Document Preparation and Text Processing
KW  - Artificial Intelligence (incl. Robotics)
KW  - Control Engineering
N1  - 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
N2  - 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]
UR  - http://dx.doi.org/10.1007/1-84628-219-5
ER  -