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Mixing Linear SVMs for Nonlinear Classification

Fu, Zhouyu; Robles-Kelly, Antonio; Zhou, Jun


In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale 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-and-conquer strategy by partitioning the feature space into subregions of linearly separable datapoints and learning a LSVM for each of...[Show more]

CollectionsANU Research Publications
Date published: 2010
Type: Journal article
Source: IEEE Transactions on Neural Networks
DOI: 10.1109/TNN.2010.2080319


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