David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Minds and Machines 12 (1):119-129 (2002)
In this paper we refer to a machine learning method that reveals all the if–then rules in the data, and on the basis of these rules issues predictions for new cases. When issuing predictions this method faces the problem of choosing from competing theories. We dealt with this problem by calculating the probability that the rule is accidental. The lower this probability, the more the rule can be `trusted' when issuing predictions. The method was tested empirically and found to be accurate. On a broader scope this approach demonstrates how the dialog between researchers in machine learning and the philosophy of science can be beneficial for both sides.
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