Letters: Statistical criteria for early-stopping of support vector machines

Authors:
Tatyana V. Bandos;Gustavo Camps-Valls;Emilio Soria-Olivas
Affiliations:
Grup de Processament Digital de Senyals, Dept. Enginyeria Electrònica, Universitat de València, C/Dr. Moliner, 50, Burjassot, València, Spain;Grup de Processament Digital de Senyals, Dept. Enginyeria Electrònica, Universitat de València, C/Dr. Moliner, 50, Burjassot, València, Spain;Grup de Processament Digital de Senyals, Dept. Enginyeria Electrònica, Universitat de València, C/Dr. Moliner, 50, Burjassot, València, Spain
Venue:
Neurocomputing
Year:
2007

Citing 4
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Abstract

This paper proposes the use of statistical criteria for early-stopping support vector machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to fourth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower computational efforts and no significant differences are observed in terms of performance and sparsity.