A comparison of consensus- and critical point-based classification strategies

In this paper we compare the consensus-based strategy (CBS) and the critical point-based strategy (CPBS) which are commonly adopted in the practice of designing classifiers. Theoretical analyses and simulation results reveal the close relationship between the kurtosis (long tailedness) of the distribution of data patterns and the performance of SVM designed with CPBS. Monte Carlo simulation results agree with the theoretical findings.