An efficient algorithm on multi-class support vector machine model selection
Peng Xu, Andrew K. Chan · 2004
Support vector machines (SVM) are very effective for general purpose pattern recognition. With carefully selected models, they have won many benchmark applications over conventional classification techniques. Current SVM model selection schemes are time consuming when they are applied to binary classification. It is practically impossible to apply these methods to multi-class SVM for detailed model selection. In this paper, we propose a scheme to effectively select models for multi-class SVMs with a globe rough selection followed by genetic algorithms (GA) for refinement. This method is applied to benchmark problems with higher accuracy rates than other approaches and is suitable for practical use.