Locating support vectors via /spl beta/-skeleton technique

Recently, support vector machine (SVM) has become a very dynamic and popular topic in the neural network community for its abilities to perform classification, estimation, and regression. One of the major tasks in the SVM algorithm is to locate the points, or rather support vectors, based on which we construct the discriminant boundary in classification task. In the process of studying the methods for finding the decision boundary, we conceive a method, /spl beta/-skeleton algorithm, which reduces the size of the training set for SVM. We describe their theoretical connections and practical implementation implications. In this paper, we also survey four different methods for classification: the SVM method, k-nearest neighbor method, /spl beta/-skeleton algorithm used in the above two methods. Compared with the methods without using /spl beta/-skeleton algorithm, prediction with the edited set obtained from /spl beta/-skeleton algorithm as the training set, does not lose the accuracy too much but reduces the real running time.