Abstract
The influencing factors of the ice hockey match result are complex, and there is a nonlinear relationship with the relevant predictive indicators. The input characteristics and parameter selection of the model have important influence on the prediction performance. Based on this, this paper proposes a support vector machine ice hockey situation prediction model based on principal component analysis, hybrid genetic algorithm and particle swarm optimization. This model uses principal component analysis to perform principal component analysis on the original features, this can reduce the dimensions of the original features effectively. Using hybrid genetic algorithm and particle swarm algorithm to optimize the parameters of support vector machine to establish a predictive model. The simulation results show that when principal component analysis is used to reduce the input features, the running time of the SVM prediction model based on principal component analysis, hybrid genetic algorithm and particle swarm optimization is reduced. Compared to a single genetic algorithm optimization parameter or a particle swarm optimization parameter support vector machine prediction model, the prediction accuracy and stability are significantly improved.
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Li, M., Xue, S., Cheng, S. (2018). Research on the Prediction Technology of Ice Hockey Based on Support Vector Machine. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_10
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DOI: https://doi.org/10.1007/978-3-030-04221-9_10
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