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Support Vector Machines for Classification and Regression

Support Vector Machines for Classification and Regression
Support Vector Machines for Classification and Regression
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.
s.n.
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Gunn, S.R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868

Gunn, S.R. (1998) Support Vector Machines for Classification and Regression s.n.

Record type: Monograph (Project Report)

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.

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More information

Published date: 1998
Additional Information: Address: Southampton, U.K.
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 256459
URI: https://eprints.soton.ac.uk/id/eprint/256459
PURE UUID: dc8bb718-df09-43c9-bc32-89e4332e8249

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Date deposited: 27 Mar 2002
Last modified: 18 Jul 2017 09:46

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Contributors

Author: S.R. Gunn

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