Construction of a Padé33 Smooth Support Vector Machine Model and Its Application

The standard SVM can be transformed into an unconstrained optimization problem, but the new objective function is not smooth, and lots of fast optimization algorithms cannot be applied to solve it. To overcome the problem, a new Pade33 approximation smooth function is put forward, based on rational approximation method. Then, a new SSVM based on Pade33 smooth function is established. Theoretical analysis proved that the smooth precision is significantly higher than existing smooth functions. Moreover, theorem proof is given to demonstrate the convergence of the new model. Finally, it is applied into the heart disease diagnosis. The experiment results indicate that the Pade33-SSVM model’s classification capability is much better.