Pairwise classification and support vector machines
Advances in kernel methods
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The directed acyclic graph support vector machines (DAGSVMs) have been shown to be able to provide classification accuracy comparable to the standard multiclass SVM extensions such as Max Wins methods. The algorithm arranges binary SVM classifiers as the internal nodes of a directed acyclic graph (DAG). Each node represents a classifier trained for the data of a pair of classes with the specific kernel. The most popular method to decide the kernel parameters is the grid search method. In the training process, classifiers are trained with different kernel parameters, and only one of the classifiers is required for the testing process. This makes the training process time-consuming. In this paper we propose using separation indexes to estimate the generalization ability of the classifiers. These indexes are derived from the inter-cluster distances in the feature spaces. Calculating such indexes costs much less computation time than training the corresponding SVM classifiers; thus the proper kernel parameters can be chosen much faster. Experiment results show that the testing accuracy of the resulted DAGSVMs is competitive to the standard ones, and the training time can be significantly shortened.