E Book

Support Vector Machines for Pattern Classification

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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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Ketersediaan

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Informasi Detail

Judul Seri

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No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

XIV, 344 p. 110 illus.online resource.

Bahasa

English

ISBN/ISSN

9781846282195

Klasifikasi

006.4

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

1st ed. 2005.

Subjek

Artificial intelligence.
Pattern recognition.
Control engineering.
Robotics.
Natural language processing (Computer science).
Natural Language Processing (NLP).
Mechatronics.
Control, Robotics, Mechatronics.

Info Detail Spesifik

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Pernyataan Tanggungjawab

by Shigeo Abe.

Versi lain/terkait

Lampiran Berkas

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