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- Publisher Website: 10.1080/17517575.2019.1592233
- Scopus: eid_2-s2.0-85064172144
- WOS: WOS:000466280800001
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Article: Efficient Parameter Selection for Support Vector Machines
Title | Efficient Parameter Selection for Support Vector Machines |
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Authors | |
Keywords | Percentiles grid search classification radial basis function (RBF) kernel hyperparameter |
Issue Date | 2019 |
Publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/17517575.asp |
Citation | Enterprise Information Systems, 2019, v. 13 n. 6, p. 916-932 How to Cite? |
Abstract | The support vector machines (SVM) is a popular classification method. Many users may not well tune hyperparameters because this step is time-consuming. However, the performance of SVM relies on the values of hyperparameters. To get around the problem, users may resort to anecdotal methods or default values set by software developers, but these methods may compromise the performance of classification accuracy. We investigate the theory that justifies P-SVM for tuning (γ,C) P-SVM significantly improved accuracy for classifying the business intelligence data. Experiments of simulation and real datasets show that P-SVM reducescomputational time substantially without much loss in accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/278969 |
ISSN | 2023 Impact Factor: 4.4 2023 SCImago Journal Rankings: 0.875 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, H-H | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Chung, W | - |
dc.date.accessioned | 2019-10-21T02:17:15Z | - |
dc.date.available | 2019-10-21T02:17:15Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Enterprise Information Systems, 2019, v. 13 n. 6, p. 916-932 | - |
dc.identifier.issn | 1751-7575 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278969 | - |
dc.description.abstract | The support vector machines (SVM) is a popular classification method. Many users may not well tune hyperparameters because this step is time-consuming. However, the performance of SVM relies on the values of hyperparameters. To get around the problem, users may resort to anecdotal methods or default values set by software developers, but these methods may compromise the performance of classification accuracy. We investigate the theory that justifies P-SVM for tuning (γ,C) P-SVM significantly improved accuracy for classifying the business intelligence data. Experiments of simulation and real datasets show that P-SVM reducescomputational time substantially without much loss in accuracy. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/17517575.asp | - |
dc.relation.ispartof | Enterprise Information Systems | - |
dc.rights | AOM/Preprint Before Accepted: his article has been accepted for publication in [JOURNAL TITLE], published by Taylor & Francis. AOM/Preprint After Accepted: This is an [original manuscript / preprint] of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. | - |
dc.subject | Percentiles | - |
dc.subject | grid search | - |
dc.subject | classification | - |
dc.subject | radial basis function (RBF) kernel | - |
dc.subject | hyperparameter | - |
dc.title | Efficient Parameter Selection for Support Vector Machines | - |
dc.type | Article | - |
dc.identifier.email | Chung, W: wchun@hku.hk | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/17517575.2019.1592233 | - |
dc.identifier.scopus | eid_2-s2.0-85064172144 | - |
dc.identifier.hkuros | 307639 | - |
dc.identifier.volume | 13 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 916 | - |
dc.identifier.epage | 932 | - |
dc.identifier.isi | WOS:000466280800001 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1751-7575 | - |