Accurate on-line support vector regression

Authors:
Junshui Ma;James Theiler;Simon Perkins
Affiliations:
Aureon Biosciences Corp., 28 Wells St., Yonkers, NY;NIS-2, Los Alamos National Laboratory, Los Alamos, NM;NIS-2, Los Alamos National Laboratory, Los Alamos, NM
Venue:
Neural Computation
Year:
2003

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Abstract

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented. In both scenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.