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Efficient parameter selection of support vector machines

Support Vector Machine (SVM) has, over the years established itself as an effective method for machine learning. SVM has strengths as such that it uses a kernel function to deal with arbitrary structured data which comprises of non-linear data sets. However, to fully optimize the benefits of using t...

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Main Authors: Ismael, Kamarulafizam, Salleh, Shaharuddin, Najeb, J. M., Jahangir Bakhteri, R. B.
Format: Book Section
Published: Institute of Electrical and Electronics Engineers 2008
Subjects:
TS Manufactures
Online Access:http://eprints.utm.my/id/eprint/12555/
http://dx.doi.org/10.1007/978-3-540-69139-6-49
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spelling my.utm.125552017-10-02T08:44:10Z http://eprints.utm.my/id/eprint/12555/ Efficient parameter selection of support vector machines Ismael, Kamarulafizam Salleh, Shaharuddin Najeb, J. M. Jahangir Bakhteri, R. B. TS Manufactures Support Vector Machine (SVM) has, over the years established itself as an effective method for machine learning. SVM has strengths as such that it uses a kernel function to deal with arbitrary structured data which comprises of non-linear data sets. However, to fully optimize the benefits of using the kernel function, one will have to fine-tune the parameters of SVM in order to achieve feasible results. However, parameter selection can get complicated as the number of parameters and the size of the dataset increases. In this paper, we propose a method to deal with effective parameter selection for SVM for optimal performance through experiments done on heart sound data using the features of IEFE extraction technique. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Ismael, Kamarulafizam and Salleh, Shaharuddin and Najeb, J. M. and Jahangir Bakhteri, R. B. (2008) Efficient parameter selection of support vector machines. In: IFMBE Proceedings. Institute of Electrical and Electronics Engineers, New York, pp. 183-186. ISBN 978-354069138-9 http://dx.doi.org/10.1007/978-3-540-69139-6-49 DOI:10.1007/978-3-540-69139-6-49
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TS Manufactures
spellingShingle TS Manufactures
Ismael, Kamarulafizam
Salleh, Shaharuddin
Najeb, J. M.
Jahangir Bakhteri, R. B.
Efficient parameter selection of support vector machines
description Support Vector Machine (SVM) has, over the years established itself as an effective method for machine learning. SVM has strengths as such that it uses a kernel function to deal with arbitrary structured data which comprises of non-linear data sets. However, to fully optimize the benefits of using the kernel function, one will have to fine-tune the parameters of SVM in order to achieve feasible results. However, parameter selection can get complicated as the number of parameters and the size of the dataset increases. In this paper, we propose a method to deal with effective parameter selection for SVM for optimal performance through experiments done on heart sound data using the features of IEFE extraction technique.
format Book Section
author Ismael, Kamarulafizam
Salleh, Shaharuddin
Najeb, J. M.
Jahangir Bakhteri, R. B.
author_facet Ismael, Kamarulafizam
Salleh, Shaharuddin
Najeb, J. M.
Jahangir Bakhteri, R. B.
author_sort Ismael, Kamarulafizam
title Efficient parameter selection of support vector machines
title_short Efficient parameter selection of support vector machines
title_full Efficient parameter selection of support vector machines
title_fullStr Efficient parameter selection of support vector machines
title_full_unstemmed Efficient parameter selection of support vector machines
title_sort efficient parameter selection of support vector machines
publisher Institute of Electrical and Electronics Engineers
publishDate 2008
url http://eprints.utm.my/id/eprint/12555/
http://dx.doi.org/10.1007/978-3-540-69139-6-49
_version_ 1643645984716095488
score 13.211869

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