A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition.Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. 
SVMs
In this work, we present a newly developed IS method called Valid Border Recognition (VBR).  VBR selects the closest heterogeneous neighbors as valid border instances and incorporates this process into the creation of a reduced Gaussian kernel matrix, thus minimizing the execution time.  To improve reliability, we propose a strengthened version of VBR (SVBR).  Based on VBR, SVBR gradually adds farther heterogeneous neighbors as complements until the Lagrange multipliers of already selected instances become stable. 
VBR
To improve reliability, we propose a strengthened version of VBR (SVBR).  Based on VBR, SVBR gradually adds farther heterogeneous neighbors as complements until the Lagrange multipliers of already selected instances become stable. 
SVBR