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A Parallel Mixture of SVMs for Very Large Scale Problems

collober{at}idiap.ch and collober{at}iro.umontreal.ca, Dalle Molle Institute for Perceptual Artificial Intelligence, 1920 Martigny, Switzerland, and Université de Montréal, DIRO, Montréal, Québec, Canada

Samy Bengio

bengio{at}idiap.ch, Dalle Molle Institute for Perceptual Artificial Intelligence, 1920 Martigny, Switzerland

Yoshua Bengio

bengioy{at}iro.umontreal.ca, Université de Montréal, DIRO, Montréal, Québec, Canada

Support vector machines (SVMs) are the state-of-the-art models for many classification problems, but they suffer from the complexity of their training algorithm, which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundred thousand examples with SVMs. This article proposes a new mixture of SVMs that can be easily implemented in parallel and where each SVM is trained on a small subset of the whole data set. Experiments on a large benchmark data set (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and surprisingly, a significant improvement in generalization was observed.

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				M. KUGLER, S. KUROYANAGI, A. S. NUGROHO, and A. IWATA CombNET-III: A Support Vector Machine Based Large Scale Classifier with Probabilistic Framework IEICE Trans D: Information, September 1, 2006; E89-D(9): 2533 - 2541. [Abstract] [PDF]

				X. Liu, L. O. Hall, and K. W. Bowyer Comments on "A Parallel Mixture of SVMs for Very Large Scale Problems" Neural Comput., July 1, 2004; 16(7): 1345 - 1351. [Abstract] [Full Text] [PDF]