Support Vector Machine–Based Prediction System for a FootballMatch Result
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 3
Abstract
Abstract: Different techniques have been used to develop result prediction systems. In particular, footballmatch result prediction systems have been developed with techniques such as artificial neural networks, naïveBayesian system, k-nearest neighbor algorithms (k-nn), and others. The choice of any technique depends on theapplication domain as well as the feature sets. The priority of a system developer or designer in most cases is toobtain a high prediction accuracy. The objective of this study is to investigate the performance of a SupportVector Machine (SVM) with respect to the prediction of football matches. Gaussian combination kernel type isused to generate 79 support vectors at 100000 iterations. 16 example football match results (data sets) weretrained to predict 15 matches. The findings showed 53.3% prediction accuracy, which is relatively low. Untilproven otherwise by other studies, an SVM-based system (as devised here) is not good enough in thisapplication domain.
Authors and Affiliations
Chinwe Peace Igiri
Keywords
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