Learning with Support Vector Machines
Campbell, Colin, Ying, Yiming
ISBN 10: 3031004248 ISBN 13: 9783031004247
Editorial: Springer, 2011
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Descripción:
2011. Paperback. . . . . . N° de ref. del artículo V9783031004247
Sinopsis:
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels
Acerca del autor: 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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Título: Learning with Support Vector Machines
Editorial: Springer
Año de publicación: 2011
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Learning with Support Vector Machines
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ISBN 10: 3031004248
ISBN 13: 9783031004247
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 interest. Nº de ref. del artículo: 608128853
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Learning with Support Vector Machines
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels. Nº de ref. del artículo: 9783031004247
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Learning with Support Vector Machines
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Springer Nature Switzerland, Springer Feb 2011, 2011
ISBN 10: 3031004248
ISBN 13: 9783031004247
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with KernelsSpringer Nature c/o IBS, Benzstrasse 21, 48619 Heek 96 pp. Englisch. Nº de ref. del artículo: 9783031004247
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Learning with Support Vector Machines
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels 96 pp. Englisch. Nº de ref. del artículo: 9783031004247
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Learning with Support Vector Machines
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Learning with Support Vector Machines (Paperback)
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Paperback. Condición: new. Paperback. Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels Support Vectors Machines have become a well established tool within machine learning. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783031004247
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Learning with Support Vector Machines (Paperback or Softback)
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Learning with Support Vector Machines (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Learning with Support Vector Machines (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Learning With Support Vector Machines
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