Two-stage svm classification for large data sets via randomly reducing and recovering training data
Xiaoou Li, Jair Cervantes, Wen Yu · 2007
Despite of good theoretic foundations and high classification accuracy of support vector machine (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel two stages SVM classification approach for large data sets by randomly selecting training data. The first stage SVM classification gets a sketch of support vector distribution. Then the neighbors of these support vectors in original data set are used as training data for the second stage SVM classification. Experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.