Support Vector Machines Applications

Offering a detailed, unified approach, this book examines advances and applications of Support Vector Machines: image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, artificial intelligence and more.

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.

Focus on current developments in the field of Support Vector Machines Illustrates critical applications of support vector machines to important real world problems Provides critical review of the state-of-the-art techniques on SVM, such as domain transfer SVM, object recognition, soft biometrics, and biomedical applications Includes supplementary material: sn.pub/extras

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

Klappentext
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.

Inhalt
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.