Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification p...
In manipulating data such as in supervised learning, we often extract new features from original features for the purpose of reducing the dimensions of feature space and achieving ...
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Sparse kernel regressors have become popular by applying the support vector method to regression problems. Although this approach has been shown to exhibit excellent generalization...
EEG segments recorded during microsleep events were transformed to the frequency domain and were subsequently clustered without the common summation of power densities in spectral ...