Support Vector Machines Applications
Published on 3. September 2016
Book
Paperback/Softback
VII, 302 pages
978-3-319-34329-7 (ISBN)
Description
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.
Reviews / Votes
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)More details
Edition
Softcover reprint of the original 1st ed. 2014
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
56 farbige Abbildungen, 31 s/w Abbildungen
VII, 302 p. 87 illus., 56 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
532 gr
ISBN-13
978-3-319-34329-7 (9783319343297)
DOI
10.1007/978-3-319-02300-7
Schweitzer Classification
Other editions
Additional editions
Yunqian Ma | Guodong Guo
Support Vector Machines Applications
Book
03/2014
Springer
€181.89
Shipment within 10-15 days
Persons
Yunqian Ma is Senior Principal Research Scientist at Honeywell Labs. Guodong Guo is an Assistant Professor at West Virginia University.
Content
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.