Reducing the Number of Training Samples for Fast Support Vector Machine Classification

Support Vector Machines (SVMs) have gained wide acceptance because of the high generalization ability for a wide range of classification applications. Although SVMs have shown potential and promising performance in classification, they have been limited by speed particularly when the training data set is large. The hyper plane constructed by SVM is dependent on only a portion of the training samples called support vectors that lie close to the decision boundary (hyper plane). Thus, removing any training samples that are not relevant to support vectors might have no effect on building the proper decision function. We propose the use of clustering techniques such as K-mean to find initial clusters that are further altered to identify non-relevant samples in deciding the decision boundary for SVM. This will help to reduce the number of training samples for SVM without degrading the classification result.