Support vector machine

a research hotspot of the pattern recognition in recent years

performs successfully in solving the nonlinear and high dimensional problems.However

training a support vector machine is equivalent to solving a linearly constrained quadratic programming problem in a number of variables equal to the number of data points.This optimization problem is known to be challenging when existing large number of training data points.Also

it is well known that the number of support vector plays an important role in the classification speed of SVM.So the method of pre-analysis efficient support vectors are used to train classifier becomes a novel task in SVM fields.In this paper

on the basis of a deep investigation into the geometry principle of support vectors and its distribution

we firstly pick out some neighbor vectors by nearest interclass distance analysis

and then select the margin vector by computing its intermixed factor of the neighbor vectors.So this method speeds up the SVM training and classifying synchronously by reducing the number of training samples and trimming the intermixed samples

while the ability of SVM remains unchanged.