Approximating support vector machine with artificial neural network for fast prediction

• Hybrid neural network (HNN), a method to accelerate prediction speed of support vector machine (SVM) is proposed.
• The proposed method approximates SVM using artificial neural network (ANN).
• The proposed method yields much faster prediction speed without compromising prediction accuracy.
• The application of this method can improve practical usability of SVM.

Support vector machine (SVM) is a powerful algorithm for classification and regression problems and is widely applied to real-world applications. However, its high computational load in the test phase makes it difficult to use in practice. In this paper, we propose hybrid neural network (HNN), a method to accelerate an SVM in the test phase by approximating the SVM. The proposed method approximates the SVM using an artificial neural network (ANN). The resulting regression function of the ANN replaces the decision function or the regression function of the SVM. Since the prediction of the ANN requires significantly less computation than that of the SVM, the proposed method yields faster test speed. The proposed method is evaluated by experiments on real-world benchmark datasets. Experimental results show that the proposed method successfully accelerates SVM in the test phase with little or no prediction loss.