Support Vector Machines Applications

  • Yunqian Ma
  • Guodong Guo

Table of contents

  1. Front Matter
    Pages i-vii
  2. Zhe Wang, Xiangyang Xue
    Pages 23-48
  3. Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera et al.
    Pages 105-153
  4. Lei Wang, Lingqiao Liu, Luping Zhou, Kap Luk Chan
    Pages 155-189
  5. Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen
    Pages 191-220
  6. Peng Li, Kap Luk Chan, Sheng Fu, Shankar M. Krishnan
    Pages 221-268

About this book

Introduction

Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

Keywords

Business Intelligence Computer Vision Kernel Machines Large Margin Classifier Learning in the Small Sample Case Learning with High Dimensionality Machine Learning Pattern Recognition Support Vector Machine

Editors and affiliations

  • Yunqian Ma
    • 1
  • Guodong Guo
    • 2
  1. 1.HoneywellGolden ValleyUSA
  2. 2.West Virginia UniversityMorgantownUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-02300-7
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-02299-4
  • Online ISBN 978-3-319-02300-7
  • About this book
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