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, as opposed to the Empirical Risk Minimisation (ERM) approach commonly employed within statistical learning methods. SRM minimises an upper boound on the generalisation error, as opposed to ERM which minmises the error on the training data. It is this difference which equips SVMs with a greater potential to generalise, which is our goal in statistical learning. The SVM can be applied to both classification and regression problems. The toolbox provides routines for support vector classification and support vector regression. A GUI is included which allows the visualisation of simple classification and regression problems. (The MATLAB optimisation toolbox, or an alternative quadratic programming routine is required.)
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Maalouf, Maher; Trafalis, Theodore B.; Adrianto, Indra: Kernel logistic regression using truncated Newton method (2011)
- Ghorai, Santanu; Hossain, Shaikh Jahangir; Mukherjee, Anirban; Dutta, Pranab K.: Newton’s method for nonparallel plane proximal classifier with unity norm hyperplanes (2010)
- Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K.: Nonparallel plane proximal classifier (2009)
- van der Schaar, Mike; Delory, Eric; André, Michel: Classification of sperm whale clicks (\textitPhysetermacrocephalus) with Gaussian-kernel-based networks (2009)