On efficient learning and classification kernel methods

Improving learning and classification efficiency has become increasingly important for machine learning. If the traditional RBF kernel is adopted, the learned kernel-based classifier usually delivers better performance by engaging a large training dataset. However, such a high performance comes at the expense of costly learning and classification complexities, which grow drastically with the training size N. To overcome this curse of dimensionality, we propose a so-called TRBF kernel(with finite intrinsic degree J) which approximates the RBF kernel. The contributions of this paper are as follows. First, the optimal classification efficiency attainable is shown to be J´ ≈ J. To improve learning efficiency, we propose a fast PDA algorithm with learning complexity linearly growing with N. We adopt pruned-PDA (PPDA) to improve the accuracy by removing harmful “anti-support” vectors from the training set. Experiments on ECG dataset showed that TRBF-PPDA delivers nearly optimal performance with very low power.