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2017 | OriginalPaper | Chapter

Optimizing Support Vector Machines for Multi-class Classification

Authors : J. K. Sahoo, Akhil Balaji

Published in: Advances in Computing and Data Sciences

Publisher: Springer Singapore

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Abstract

The accuracy obtained when classifying multi-class data depends on the classifier and the features used for training the classifier. The parameters passed to the classifier and feature selection techniques can help improve accuracy. In this paper we propose certain dataset and classifier optimization to help improve the accuracy when classifying multi-class data. These optimization also help in reducing the training time.

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Metadata
Title
Optimizing Support Vector Machines for Multi-class Classification
Authors
J. K. Sahoo
Akhil Balaji
Copyright Year
2017
Publisher
Springer Singapore
Book
Advances in Computing and Data Sciences

Print ISBN: 978-981-10-5426-6

Electronic ISBN: 978-981-10-5427-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-5427-3_42

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