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    Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing

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    Author
    Chen, Hsinchun
    Buntin, P.
    She, Linlin
    Sutjahjo, S.
    Sommer, C.
    Neely, D.
    Issue Date
    1994-12
    Submitted date
    2004-10-13
    Keywords
    Artificial Intelligence
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Machine-learning algorithms
    
    Metadata
    Show full item record
    Citation
    Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing 1994-12, 9(6):21-27 IEEE Expert
    Publisher
    IEEE
    Journal
    IEEE Expert
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105472
    Abstract
    For our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
    Type
    Journal Article (Paginated)
    Language
    en
    Collections
    DLIST

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