Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A neural root finder of polynomials based on root moments
Neural Computation
An Algorithm for Finding Nearest Neighbors
IEEE Transactions on Computers
A constructive approach for finding arbitrary roots of polynomials by neural networks
IEEE Transactions on Neural Networks
Zeroing polynomials using modified constrained neural network approach
IEEE Transactions on Neural Networks
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This paper proposes two methods which take advantage of k-mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM). The first method uses k-mean clustering to construct a dataset of much smaller size than the original one as the actual input dataset to train SVM. The second method aims at reducing the number of SVs by which the decision function of the SVM classifier is spanned through k-mean clustering. Finally, Experimental results show that this improved algorithm has better performance than the standard Sequential Minimal Optimization (SMO) algorithm.