Williams, Janett and Li, Yan (2008) A case study using neural networks algorithms: horse racing predictions in Jamaica. In: ICAI 2008: International Conference on Artificial Intelligence , 14-17 Jul 2008, Las Vegas, NV. United States.
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Neural networks (NNs) have been applied to predict many complex problems, such as horse racing prediction. This study investigates the performances of four supervised neural network algorithms in horse racing. We employed the Backpropagation (BP), Quasi_Newton (QN), LevenbergMarquardt (LM) and Conjugate Gradient Descent (CGD) learning algorithms to real horse racing data collected from Caymans Race Track, Jamaica. The training and testing data comes from 143 actual races collected from 1 January to 16 June 2007. The experimental results demonstrate that all algorithms can provide acceptable predictions with an accuracy of 74%. The BP algorithm slightly performs better than the other three algorithms but needs a longer training time and more parameter selections.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||No evidence of copyright restrictions.|
|Uncontrolled Keywords:||neural network; back propagation algorithm; machine learning; race tracks; supervised neural networks; robot learning|
|Subjects:||280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic|
|Depositing User:||Dr Yan Li|
|Date Deposited:||06 Aug 2008 05:57|
|Last Modified:||02 Jul 2013 23:05|
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