Nonparallel Hyperplanes Support Vector Machine for Multi-class Classification

In this paper, we proposed a nonparallel hyperplanes classifier for multi-class classification, termed as NHCMC. This method inherits the idea of multiple birth support vector machine(MBSVM), that is the “max” decision criterion instead of the “min” one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved efficiently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the efficiency of our method in multi-class classification accuracy