Support vector machine model for regression applied to the estimation of the creep ruptura stress in ferritic steels

Having as antecedent the use of artificial neural networks (ANN) in the estimation of the creep rupture stress in ferritic steels, new experiments have been developed using Support Vector Machine for Regression (SVMR), a recently method developed into the machine learning field. A comparative analysis between both methods established that SVMR have a better behavior in the problematic of creep. The results are explained theoretically and finally, the use of a model of SVMR that uses a polynomial kernel of third grade and a control capacity constant of 100, is proposed.