Neural network learning and expert systems
Neural network learning and expert systems
Pairwise classification and support vector machines
Advances in kernel methods
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Designing nonlinear classifiers through minimizing VC dimension bound
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Original multiclass kernel perceptron algorithm is time consuming in its training and discriminating procedures. In this paper, for each class its reduced kernel-based discriminant function is defined only by training samples from this class itself and a bias term, which means that except for bias terms the number of variables to be solved is always equal to the number of total training samples regardless of class number and the final discriminant functions are sparse. Such a strategy can speed up the training and discriminating procedures effectively. Further an additional iterative procedure with a decreasing learning rate is designed to improve the classification accuracy for the nonlinearly separable case. The experimental results on five benchmark datasets using ten-fold cross validation show that our modified training methods run at least two times and at most five times as fast as original algorithm does.