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 - London, 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
- Publish Location: London
“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 - 9781846282195
- ISBN-10: 1852339292
- All ISBNs: 1852339292 - 9781852339296 - 9781846282195
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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