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Support Vector Machines Applications [electronic resource] / edited by Yunqian Ma, Guodong Guo.

Contributor(s): Ma, Yunqian [editor.] | Guo, Guodong [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VII, 302 p. 87 illus., 56 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319023007.Subject(s): Engineering | Computer organization | Computer communication systems | Computational intelligence | Complexity, Computational | Electrical engineering | Engineering | Signal, Image and Speech Processing | Computer Communication Networks | Complexity | Computational Intelligence | Computer Systems Organization and Communication Networks | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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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