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Anytime query-tuned kernel machine classifiers via Cholesky factorizationWe recently demonstrated 2 to 64-fold query-time speedups of Support Vector Machine and Kernel Fisher classifiers via a new computational 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.
Document ID
20060030410
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
DeCoste, D.
Date Acquired
August 23, 2013
Publication Date
December 9, 2002
Distribution Limits
Public
Copyright
Other
Keywords
Kernel Cholesky MNIST

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