Fast pruning superfluous support vectors in SVMs.

Highlights: [•] SVMs often contain many SVs, which reduce runtime speeds of decision functions. [•] The method removes redundant SVs in one iteration, greatly improving pruning speed. [•] The existence and uniqueness of the fast pruning coefficients are shown. [•] The nexus of primal and dual optimizations is illustrated geometrically. [•] The method can be applied to other kernel-based machines without modifications. [Copyright &y& Elsevier]