Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Edition: 1 edition
Publisher: Springer
Published: July 29, 2005
Language: eng
Format: Hardcover
Number of pages: 357
ISBN-13: 9781852339296
Subjects: Pattern recognition; Machine learning; Computers - Desktop Publishing; Computers; Computer Books: General; Text processing (Computer science); Pattern recognition systems; Artificial Intelligence - General; Desktop Publishing - General; Engineering - Electrical & Electronic; Computers / Document Management; Fuzzy systems; Kernel methods; Neural networks; Pattern classification; Support vector machines; Text processing (Computer scie
First sentence: Pattern classification is to classify some object into one of the given categories called classes.
Description: 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].