This paper presents a method to speed up support vec- tor classification, especially important when data is high- dimensional. Unlike previous approaches which focus on less support vectors, we partition the data space into lo- cal regions, and perform approximation by linear functions. The experimental results on 31 datasets show that the per- formance degrades marginally, while the speedup is signif- icant, up to three orders of magnitude.
['Thang V. Pham', 'Arnold W. M. Smeulders']
Metric tree partitioning and Taylor approximation for fast support vector classification