Springer 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. Review 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) From the Back Cover 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. About the Author Yunqian Ma is Senior Principal Research Scientist at Honeywell Labs. Guodong Guo is an Assistant Professor at West Virginia University.