Anytime query-tuned kernel machine classifiers via Cholesky factorization
Dennis DeCoste · 2002
We recently demonstrated 2 to 64-fold query-time speedups of Sup-port Vector Machine and Kernel Fisher classifiers via a new com-putational geometry method for anytime output bounds (DeCoste, 2002). This new paper refines our approach in two key ways. First, we introduce a simple linear algebra formulation based on Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on benchmark datasets. 1