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RESEARCH PRODUCT
Statistical criteria for early-stopping of support vector machines
Tatyana V. BandosGustau Camps-vallsEmilio Soria-olivassubject
Mathematical optimizationEarly stoppingStructured support vector machinebusiness.industryCognitive NeuroscienceMachine learningcomputer.software_genreRegressionProbability vectorComputer Science ApplicationsSupport vector machineRelevance vector machineArtificial IntelligenceConvergence (routing)MinificationArtificial intelligencebusinesscomputerMathematicsdescription
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.
year | journal | country | edition | language |
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2007-08-01 | Neurocomputing |