Kernel Functions for the Support Vector Machine: Comparing Performances on Crude Oil Price Data
The purpose of this research is to broaden the theoretic understanding of the effects of kernel functions for the support vector machine on crude oil price data. The performances of five (5) kernel functions of the support vector machine were compared. The analysis of variance was used for validating the results and we take additional steps to study the Post Hoc. Findings emanated from the research indicated that the performance of the wave kernel function was statistically significantly better than the radial basis function, polynomial, exponential, and sigmoid kernel functions. Computational efficiency of the wave activation function was poor compared with the other kernel functions in the study. This research could provide a better understanding of the behavior of the kernel functions for support vector machine on the crude oil price dataset. The study has the potentials of triggering interested researchers to propose a novel methodology that can advance crude oil prediction accuracy.
KeywordsRadial basis function Polynomial Exponential Sigmoid Wave Crude oil price
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