Abstract
Uncertainty is inevitable in problem solving and decision making. One way to reduce it is by seeking the advice of an expert. When we use computers to reduce uncertainty, the computer itself can become an “expert” in a specific field through a variety of methods. One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. Machine learning usually encompasses different types of solutions, such as decision trees, production rules, and neural networks.
© 1994 IEEE. Reprinted, with permission, from (Chen, H., P. Rinde, et al. 1994. Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment in Greyhound Racing. IEEE Expert 9(6): 21–27).
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Schumaker, R.P., Solieman, O.K., Chen, H. (2010). Greyhound Racing Using Neural Networks: A Case Study. In: Sports Data Mining. Integrated Series in Information Systems, vol 26. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6730-5_10
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DOI: https://doi.org/10.1007/978-1-4419-6730-5_10
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