Learning with Support Vector Machines

Author:   Colin Campbell ,  Yiming Ying
Publisher:   Springer International Publishing AG
ISBN:  

9783031004247


Pages:   83
Publication Date:   10 February 2011
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Learning with Support Vector Machines


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Author:   Colin Campbell ,  Yiming Ying
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Weight:   0.200kg
ISBN:  

9783031004247


ISBN 10:   3031004248
Pages:   83
Publication Date:   10 February 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.
Language:   English

Table of Contents

Support Vector Machines for Classification.- Kernel-based Models.- Learning with Kernels.

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Author Information

Dr. Colin Campbell holds a BSc degree in physics from Imperial College, London, and a PhD in mathematics from King's College, London. He joined the Faculty of Engineering at the University of Bristol in 1990 where he is currently a Reader. His main interests are in machine learning and algorithm design. Current topics of interest include kernel-based methods, probabilistic graphical models and the application of machine learning techniques to medical decision support and bioinformatics. His research is supported by the EPSRC, Cancer Research UK, the MRC and PASCAL2. Dr. Yiming Ying received his BSc degree in mathematics from Zhejiang University (formally, Hangzhou University) in 1997 and his PhD degree in mathematics from Zhejiang University in 2002, Hangzhou, China. He is currently a Lecturer (Assistant Professor) in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His research interests include machine learning, pattern analysis, convex optimization, probabilistic graphical models and applications to bioinformatics and computer vision.

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