On-line independent support vector machines

Orabona F, Castellini C, Caputo B, Jie L, Sandini G (2010)

Publication Type: Journal article

Publication year: 2010

Journal

Pattern Recognition Elsevier

Book Volume: 43

Pages Range: 1402-1412

Journal Issue: 4

DOI: 10.1016/j.patcog.2009.09.021

Abstract

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. © 2009 Elsevier Ltd. All rights reserved.

Authors with CRIS profile

Claudio Castellini

Involved external institutions

idiap Research Institute

Switzerland (CH) University of Genova / Università degli Studi di Genova

Italy (IT)

How to cite

APA:

Orabona, F., Castellini, C., Caputo, B., Jie, L., & Sandini, G. (2010). On-line independent support vector machines. Pattern Recognition, 43(4), 1402-1412. https://dx.doi.org/10.1016/j.patcog.2009.09.021

MLA:

Orabona, Francesco, et al. "On-line independent support vector machines." Pattern Recognition 43.4 (2010): 1402-1412.

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