SEARCH RESULT

Support vector machine is an effective machine learning method based on statistical learning theory and used for classification problems. The optimization of the parameter is very important in order to increase the classification accuracy. Meta-heuristic methods are one of the main optimization approaches that can be applied in this context and have been used frequently for parameter optimization in recent years. These methods are generally particle swarm optimization, genetic algorithm, grid search method, differential evolution algorithm, ant colony optimization. In this study, support vector machine parameter optimization studies between 2010- 2019 were investigated. According to the results of these studies, it was observed that parameter optimization through meta-heuristic methods significantly increased the rate of classification accuracy of classifier and significantly reduced the workload.

International Data Science & Engineering Symposium
IDSES

Zübeyir ÖZKORUCU Turgut ÖZSEVEN