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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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Detalles Bibliográficos
Autores principales: Campbell, Colin, Ying, Yiming
Lenguaje:eng
Publicado: Morgan & Claypool Publishers 2010
Materias:
Acceso en línea:http://cds.cern.ch/record/1486593
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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
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institution Organización Europea para la Investigación Nuclear
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publishDate 2010
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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