Support Vector Machines Applications (eBook, PDF)

Redaktion: Ma, Yunqian; Guo, Guodong

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 302
• Erscheinungstermin: 12. Februar 2014
• Englisch
• ISBN-13: 9783319023007
• Artikelnr.: 43798397

Autorenporträt

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

Inhaltsangabe

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.

Rezensionen

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)