Support Vector Machines for Pattern Classification (Advances in Pattern Recognition) - Info and Reading Options
By Shigeo Abe
"Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)" is published by Springer in July 29, 2005, it has 357 pages and the language of the book is English.
“Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)” Metadata:
- Title: ➤ Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
- Author: Shigeo Abe
- Language: English
- Number of Pages: 357
- Publisher: Springer
- Publish Date: July 29, 2005
“Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)” Subjects and Themes:
- Subjects: ➤ Text processing (Computer science) - Support vector machines - Pattern recognition systems - Machine learning - Computer science - Artificial intelligence - Text processing (Computer science - Optical pattern recognition - Pattern Recognition - Document Preparation and Text Processing - Artificial Intelligence (incl. Robotics) - Control Engineering
Edition Specifications:
- Format: Hardcover
- Weight: 1.4 pounds
- Dimensions: 9.3 x 6.3 x 0.9 inches
Edition Identifiers:
- The Open Library ID: OL8974488M - OL3342815W
- Library of Congress Control Number (LCCN): 2005040265
- ISBN-13: 9781852339296
- ISBN-10: 1852339292
- All ISBNs: 1852339292 - 9781852339296
AI-generated Review of “Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)”:
Snippets and Summary:
Pattern classification is to classify some object into one of the given categories called classes.
"Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)" Description:
The Open Library:
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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