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 distri...

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Hauptverfasser:	Weichao Xu, Rubao Ma, Qinruo Wang
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Bernoulli distribution (BD) Consensus-based strategy (CBS) Critical point-based strategy (CPBS) Educational institutions Fisher linear discrimination analysis (FLDA) Gaussian distribution Laplace distribution (LD) Monte Carlo methods Normal distribution (ND) Random variables Simulation Support vector machine (SVM) Support vector machines Training Uniform distribution (UD)
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Zusammenfassung:	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.
DOI:	10.1109/CyberneticsCom.2012.6381616