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Learning with Support Vector Machines
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 th...
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Lenguaje: | eng |
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Morgan & Claypool Publishers
2010
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Acceso en línea: | http://cds.cern.ch/record/1486593 |
_version_ | 1780926153438003200 |
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author | Campbell, Colin Ying, Yiming |
author_facet | Campbell, Colin Ying, Yiming |
author_sort | Campbell, Colin |
collection | CERN |
description | 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 a |
id | cern-1486593 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2010 |
publisher | Morgan & Claypool Publishers |
record_format | invenio |
spelling | cern-14865932021-04-22T00:16:48Zhttp://cds.cern.ch/record/1486593engCampbell, ColinYing, YimingLearning with Support Vector MachinesComputing and ComputersSupport 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 aMorgan & Claypool Publishersoai:cds.cern.ch:14865932010 |
spellingShingle | Computing and Computers Campbell, Colin Ying, Yiming Learning with Support Vector Machines |
title | Learning with Support Vector Machines |
title_full | Learning with Support Vector Machines |
title_fullStr | Learning with Support Vector Machines |
title_full_unstemmed | Learning with Support Vector Machines |
title_short | Learning with Support Vector Machines |
title_sort | learning with support vector machines |
topic | Computing and Computers |
url | http://cds.cern.ch/record/1486593 |
work_keys_str_mv | AT campbellcolin learningwithsupportvectormachines AT yingyiming learningwithsupportvectormachines |