Pinpointing good hypotheses with heuristics
Artificial intelligence and statistics
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Concept formation knowledge and experience in unsupervised learning
Concept formation knowledge and experience in unsupervised learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to Expert Systems
Introduction to Expert Systems
Artificial Intelligence: Where Has it Been, and Where is it Going?
IEEE Transactions on Knowledge and Data Engineering
Combining Evolutionary, Connectionist, and Fuzzy Classification Algorithms for Shape Analysis
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Horse racing prediction using artificial neural networks
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Machine learning the harness track: Crowdsourcing and varying race history
Decision Support Systems
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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. We compared the prediction performances of three human track experts with those of two machine learning techniques: a decision tree building algorithm (ID3), and a neural network learning algorithm (backpropagation). For our research, we investigated a 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, a complex domain that involves about 50 performance variables for eight competing dogs in a race. For every race, each dog's past history is complete and freely available to bettors. This is a large amount of historical information-some accurate and relevant, some noisy and irrelevant-that must be filtered, selected, and analyzed to assist in making a prediction. This large search space 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.