Support Vector Machines Applications

1. Front Matter

   Pages i-vii

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2. Augmented-SVM for Gradient Observations with Application to Learning Multiple-Attractor Dynamics

   • Ashwini Shukla, Aude Billard

   Pages 1-21

3. Multi-Class Support Vector Machine

   • Zhe Wang, Xiangyang Xue

   Pages 23-48

4. Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning

   • Zhaohong Deng, Shitong Wang

   Pages 49-103

5. Security Evaluation of Support Vector Machines in Adversarial Environments

   • Battista Biggio, Igino Corona, Blaine Nelson, Benjamin I. P. Rubinstein, Davide Maiorca, Giorgio Fumera et al.

   Pages 105-153

6. Application of SVMs to the Bag-of-Features Model: A Kernel Perspective

   • Lei Wang, Lingqiao Liu, Luping Zhou, Kap Luk Chan

   Pages 155-189

7. Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination

   • Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

   Pages 191-220

8. Kernel Machines for Imbalanced Data Problem in Biomedical Applications

   • Peng Li, Kap Luk Chan, Sheng Fu, Shankar M. Krishnan

   Pages 221-268

9. Soft Biometrics from Face Images Using Support Vector Machines

   • Guodong Guo

   Pages 269-302

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

• 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
• complexity

From the book reviews:

“The book brings substantial contributions to the field of SVMs from both theoretical and practical points of view. The concepts and methods are presented in a clear and accessible way, and the illustrative examples and applications provide a valuable source of inspiration for similar developments. … This book is of considerable value to researchers in the fields of machine learning, data mining, and statistical pattern recognition.” (L. State, Computing Reviews, August, 2014)

• Honeywell, Golden Valley, USA

  Yunqian Ma

• West Virginia University, Morgantown, USA

  Guodong Guo

Yunqian Ma is Senior Principal Research Scientist at Honeywell Labs. Guodong Guo is an Assistant Professor at West Virginia University.

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