High-Performance Large-Scale SVM-based Multiclass Classification

Abstract. A typical characteristic of modern applied multiclass classification problems is the large scale. It significantly complicates or even makes it impossible an application of such popular, convenient and well-interpreted method as Support Vector Machines (SVM), which is well-proven for small-size classification problems. In this connection the actual problem is to increase SVM’s computational performance. The Double-Layer Smart Sampling SVM (DLSS-SVM) method allows to reduce the training time of multiclass SVM via double using the smart sampling technique. This paper proposes the high-performance version of DLSS-SVM (HP-DLSS-SVM). It is based on two-level parallel computing scheme, which exploits useful DLSS-SVM properties and computing system capabilities more fully. Experimental investigation of the proposed HPDLSS-SVM method was made on three large handwritten digit images data sets of different size. Experiments show that the proposed approach allows to essentially decrease training and testing times and at that to maintain the obtained recognition accuracy close to the best.