Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors
Support vector machines (and other kernel machines) offer robust modern machine learning methods for nonlinear classification. However, relative to other alternatives (such as linear methods, decision trees and neural networks), they can be orders of magnitude slower at query-time. Unlike existing methods that attempt to speedup querytime, such as reduced set compression (e.g. (Burges, 1996)) and anytime bounding (e.g. (DeCoste, 2002), we propose a new and efficient approach based on treating the kernel machine classifier as a special form of k nearest-neighbor. Our approach improves upon a traditional k-NN by determining at query-time a good k for each query, based on pre-query analysis guided by the original robust kernel machine. We demonstrate effectiveness on high-dimensional benchmark MNIST data, observing a greater than 100- fold reduction in the number of SVs required per query (amortized over all 45 pairwise MNIST digit classifiers), with no extra test errors (in fact, it happens to make 4 fewer).