Article ID Journal Published Year Pages File Type
1713290 Journal of Systems Engineering and Electronics 2006 7 Pages PDF
Abstract
Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.
Related Topics
Physical Sciences and Engineering Engineering Control and Systems Engineering
Authors
, , ,