A novel parallel reduced support vector machine

Support Vector Machine (SVM) has been applied in many classification systems successfully. However, it is restricted to work well on the small sample sets. This paper presents a novel parallel reduced support vector machine. The proposed algorithm consists of three parts: firstly dividing the training samples into some grids; then training sample subset through density clustering; and finally classifying the samples. After clustering the positive samples and negative samples, this algorithm picks out such samples that locate on the edge of clusters as reduced sample subset. Then, we sum up these reduced sample subsets as reduced sample set. These reduced samples are then used to find the support vectors and the optimal classifying hyperplane by support vector machine. Additionally, it also improves classification precision by reducing the percentage of counterexamples in kernel object F-area. Experiment results show that not only efficiency but also classification precision are improved, compared with other algorithms.