In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of largescale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-andconquer strategy by partitioning the feature space into subregions of linearly separable datapoints and learning a LSVM for each of these regions. We do this implicitly by deriving a generative model over the joint data and label distributions. Consequently, we can impose priors on the mixing coefficients and do implicit model selection in a top-down manner during the parameter estimation process. This guarantees the sparsity of the learned model. Experimental results show that the proposed method can achieve the efficiency of LSVMs in the prediction phase while still providing a classification performance comparable to nonlinear SVMs.
REF analyzed a problem of combining linear SVMs (LSVMs) to classify nonlinear data.
Mixing Linear SVMs for Nonlinear Classification
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Computer Science Medicine