Weighted support vector machine method suitable for weighted sample set

In order to deal with the different importances of the samples in a sample set in pattern recognition, a weighted support vector machine method is presented and analyzed in this paper. Samples' weights are properly solved through introducing the concept of weighted distance between weighted sample and hyperplane. Under the circumstances that weight distribution is not presented explicitly, an empirical method based on interclass central distance is presented to estimate the weights of samples set. Cross validation simulation on man-made and real data set shows the weighted support vector machine is a new applicable classification method.