A Novel SVM Classification Method for Large Data Sets
Xiaoou Li, Jair Cervantes, Wen Yu · 2010
Normal support vector machine (SVM) algorithms are not suitable for classification of large data sets because of high training complexity. This paper introduces a novel SVM classification approach for large data sets. It has two phases. In the first phase, an approximate classification is obtained by SVM using fast clustering techniques to select the training data from the original data set. In the second phase, the classification is refined by using only data near to the approximate hyper plane obtained in the first phase. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers. The proposed classifier has distinctive advantages on dealing with huge data sets.