Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction

The applicability of non-linear support vector machines (SVMs) has been limited in largescale data collections because of their linear prediction complexity to the size of support vectors. We propose an efficient prediction algorithm with performance guarantee for non-linear SVMs, termed AdaptSVM. It can selectively collapse the kernel function computation to a reduced set of support vectors, compensated by an additional correction term that can be easily computed on-line. It also allows adaptive fall-back to original kernel computation based on its estimated variance and maximum error tolerance. In addition to theoretical analysis, we empirically evaluate on multiple large-scale datasets to show that the proposed algorithm can speed up the prediction process up to 10 times with only < 0.5% accuracy loss.