On-line Independent Support Vector Machines
Francesco Orabona, Claudio Castellini, Barbara Caputo, Luo Jie and Giulio Sandini
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.