Support Vector Machines Applications
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Beschreibung
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.
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
Autor*in
Yunqian Ma
Themen in »Support Vector Machines 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
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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)()