A pattern classification method based on GA and SVM
Zhang Xiangrong, Fang Liu · 2003
A method for pattern classification on large-scale training data is presented in this paper, which is based upon the genetic algorithm (GA) and support vector machine (SVM). The initial training data are optimized with GA in order to find a sample subset including the important samples that can preserve or improve the discrimination ability of SVM. Training on the subset is equal to that on the initial sample sets. The training time is greatly shortened. Following the result, we take advantage of the excellent classification performance of SVM to accomplish the pattern classification.