Support Vector Machines Applications

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.

Product Details

ISBN-13: 9783319022994
Publisher: Springer International Publishing
Publication date: 02/12/2014
Edition description: 2014
Pages: 302
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

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

Table of Contents

Augmented-SVM for gradient observations with application to learning multiple-attractor dynamics.- Multi-class Support Vector Machine.- Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning.- Security Evaluation of Support Vector Machines in Adversarial Environments.- Application of SVMs to the Bag-of-features Model— A Kernel Perspective.- Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination.- Kernel Machines for Imbalanced Data Problem and the Use in Biomedical Applications.- Soft Biometrics from Face Images using Support Vector Machines.

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