Support Vector Machine: Principles, Parameters, and Applications
© 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the late 1990s and successfully applied to many engineering related applications. In this chapter, attempts were made to introduce the SVM, its principles, structures, and parameters. The issue of selecting...
| Main Authors: | , |
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| Format: | Book Chapter |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/58416 |
| _version_ | 1848760254580391936 |
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| author | Gholami, Raoof Fakhari, N. |
| author_facet | Gholami, Raoof Fakhari, N. |
| author_sort | Gholami, Raoof |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the late 1990s and successfully applied to many engineering related applications. In this chapter, attempts were made to introduce the SVM, its principles, structures, and parameters. The issue of selecting a kernel function and other associated parameters of SVMs was also raised and applications from different petroleum and mining related tasks were brought to show how those parameters can be properly selected. It seems that the cross-validation approach would be the best technique for parameter selections of SVMs but few other concerns such as running time must not be neglected. |
| first_indexed | 2025-11-14T10:12:51Z |
| format | Book Chapter |
| id | curtin-20.500.11937-58416 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:12:51Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-584162017-11-24T05:46:26Z Support Vector Machine: Principles, Parameters, and Applications Gholami, Raoof Fakhari, N. © 2017 Elsevier Inc. All rights reserved. Support Vector Machine (SVM) has been introduced in the late 1990s and successfully applied to many engineering related applications. In this chapter, attempts were made to introduce the SVM, its principles, structures, and parameters. The issue of selecting a kernel function and other associated parameters of SVMs was also raised and applications from different petroleum and mining related tasks were brought to show how those parameters can be properly selected. It seems that the cross-validation approach would be the best technique for parameter selections of SVMs but few other concerns such as running time must not be neglected. 2017 Book Chapter http://hdl.handle.net/20.500.11937/58416 10.1016/B978-0-12-811318-9.00027-2 restricted |
| spellingShingle | Gholami, Raoof Fakhari, N. Support Vector Machine: Principles, Parameters, and Applications |
| title | Support Vector Machine: Principles, Parameters, and Applications |
| title_full | Support Vector Machine: Principles, Parameters, and Applications |
| title_fullStr | Support Vector Machine: Principles, Parameters, and Applications |
| title_full_unstemmed | Support Vector Machine: Principles, Parameters, and Applications |
| title_short | Support Vector Machine: Principles, Parameters, and Applications |
| title_sort | support vector machine: principles, parameters, and applications |
| url | http://hdl.handle.net/20.500.11937/58416 |