Choosing from competing theories in computerised learning

Minds and Machines 12 (1):119-129 (2002)
Abstract
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)
Options
 Save to my reading list
Follow the author(s)
My bibliography
Export citation
Find it on Scholar
Edit this record
Mark as duplicate
Revision history Request removal from index
 
Download options
PhilPapers Archive


Upload a copy of this paper     Check publisher's policy on self-archival     Papers currently archived: 9,357
External links
  • Through your library Configure
    References found in this work BETA

    No references found.

    Citations of this work BETA

    No citations found.

    Similar books and articles
    Analytics

    Monthly downloads

    Added to index

    2009-01-28

    Total downloads

    9 ( #128,855 of 1,088,777 )

    Recent downloads (6 months)

    0

    How can I increase my downloads?

    My notes
    Sign in to use this feature


    Discussion
    Start a new thread
    Order:
    There  are no threads in this forum
    Nothing in this forum yet.