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Support Vector Machines Applications

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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.

Author(s): Yunqian Ma, Guodong Guo (eds.)
Edition: 1
Publisher: Springer International Publishing
Year: 2014

Language: English
Pages: 302
Tags: Signal, Image and Speech Processing; Computer Communication Networks; Complexity; Computational Intelligence; Computer Systems Organization and Communication Networks; Communications Engineering, Networks

Front Matter....Pages i-vii
Augmented-SVM for Gradient Observations with Application to Learning Multiple-Attractor Dynamics....Pages 1-21
Multi-Class Support Vector Machine....Pages 23-48
Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning....Pages 49-103
Security Evaluation of Support Vector Machines in Adversarial Environments....Pages 105-153
Application of SVMs to the Bag-of-Features Model: A Kernel Perspective....Pages 155-189
Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination....Pages 191-220
Kernel Machines for Imbalanced Data Problem in Biomedical Applications....Pages 221-268
Soft Biometrics from Face Images Using Support Vector Machines....Pages 269-302