Expert prediction, symbolic learning, and neural networks an experiment on greyhound racing

Hsinchun Chen, Peter Buntin Rinde, Linlin She, Siunie Sutjahjo, Chris Sommer, Daryl Neely

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Uncertainty, an inevitable problem in problem solving can be reduced by seeking the advice of an expert in terms of computer algorithms such as machine learning. Machine learning encompasses different types of solutions. In the present investigation, a different problem-solving scenario called game playing is investigated. For this purpose, greyhound racing, a complex domain that involves almost 50 performance variables for eight competing dogs in a race is considered. For every race, each dog's past history is complete and freely available to bettors. This article discusses the experimental procedures as well as the results obtained in the process.

Original languageEnglish (US)
JournalIEEE Expert-Intelligent Systems and their Applications
Volume9
Issue number6
StatePublished - Dec 1994

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Learning systems
Neural networks
Experiments
Uncertainty

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Expert prediction, symbolic learning, and neural networks an experiment on greyhound racing. / Chen, Hsinchun; Rinde, Peter Buntin; She, Linlin; Sutjahjo, Siunie; Sommer, Chris; Neely, Daryl.

In: IEEE Expert-Intelligent Systems and their Applications, Vol. 9, No. 6, 12.1994.

Research output: Contribution to journalArticle

Chen, Hsinchun ; Rinde, Peter Buntin ; She, Linlin ; Sutjahjo, Siunie ; Sommer, Chris ; Neely, Daryl. / Expert prediction, symbolic learning, and neural networks an experiment on greyhound racing. In: IEEE Expert-Intelligent Systems and their Applications. 1994 ; Vol. 9, No. 6.
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