PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On-line Independent Support Vector Machines
Francesco Orabona, Claudio Castellini, Barbara Caputo, Luo Jie and Giulio Sandini
Pattern Recognition Volume 43, Number 4, pp. 1402-1412, 2010.


Support vector machines (SVMs) are one of the most successful algorithms for classification. However, due to their space and time requirements, they are not suitable for on-line learning, that is, when presented with an endless stream of training observations. In this paper we propose a new on-line algorithm, called on-line independent support vector machines (OISVMs), which approximately converges to the standard SVM solution each time new observations are added; the approximation is controlled via a user-defined parameter. The method employs a set of linearly independent observations and tries to project every new observation onto the set obtained so far, dramatically reducing time and space requirements at the price of a negligible loss in accuracy. As opposed to similar algorithms, the size of the solution obtained by OISVMs is always bounded, implying a bounded testing time. These statements are supported by extensive experiments on standard benchmark databases as well as on two real-world applications, namely place recognition by a mobile robot in an indoor environment and human grasping posture classification.

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Subjects:Learning/Statistics & Optimisation
ID Code:5724
Deposited By:Francesco Orabona
Deposited On:08 March 2010