Support Vector Machines for Classification and Regression
Gunn, S.R. (1998) Support Vector Machines for Classification and Regression.
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Description/Abstract
The foundations of Support Vector Machines (SVM) have been developed by Vapnik and are gaining popularity due to many attractive features, and promising empirical performance. The formulation embodies the Structural Risk Minimisation (SRM) principle, which in our work has been shown to be superior to traditional Empirical Risk Minimisation (ERM) principle employed by conventional neural networks. SRM minimises an upper bound on the VC dimension (generalisation error), as opposed to ERM which minimises the error on the training data. It is this difference which equips SVMs with a greater ability to generalise, which is our goal in statistical learning. SVMs were developed to solve the classification problem, but recently they have been extended to the domain of regression problems.
| Item Type: | Monograph (Technical Report) |
|---|---|
| Additional Information: | Address: Southampton, U.K. |
| Divisions: | Faculty of Physical and Applied Science > Electronics and Computer Science > Electronic & Software Systems |
| Item ID: | 256459 |
| Date Deposited: | 27 Mar 2002 |
| Last Modified: | 01 Mar 2012 10:47 |
| Contributors: | Gunn, S.R. (Author) |
| Date: | 1998 |
| Additional Information: | Address: Southampton, U.K. |
| Status: | Published |
| Further Information: | Google Scholar |
| URI: | http://eprints.soton.ac.uk/id/eprint/256459 |
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