David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Knud Illeris (ed.) (2009). Contemporary Theories of Learning: Learning Theorists -- In Their Own Words. Routledge.
Stephen G. Brush (1994). Dynamics of Theory Change: The Role of Predictions. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:133 - 145.
Malcolm R. Forster (1999). How Do Simple Rules `Fit to Reality' in a Complex World? Minds and Machines 9 (4):543-564.
Domenic Berducci (2010). Teaching, Learning, Describing, and Judging Via Wittgensteinian Rules: Connections to Community. [REVIEW] Human Studies 33 (4):445-463.
R. G. Swinburne (1970). Choosing Between Confirmation Theories. Philosophy of Science 37 (4):602-613.
Victoria Parker (2010). Making Choices. Heinemann Library.
F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
Michael Wertheimer (1988). Obstacles to the Integration of Competing Theories in Psychology. Philosophical Psychology 1 (1):131 – 137.
Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
Added to index2009-01-28
Total downloads10 ( #148,407 of 1,102,812 )
Recent downloads (6 months)1 ( #296,987 of 1,102,812 )
How can I increase my downloads?