Abstract
Kearns et al. (1997) in an earlier paper presented an empirical evaluation of model selection methods on a specialized version of the segmentation problem. The inference task was the estimation of a predefined Boolean function on the real interval [0,1] from a noisy random sample. Three model selection methods based on the Guaranteed Risk Minimization, Minimum Description Length (MDL) Principle and Cross Validation were evaluated on samples with varying noise levels. The authors concluded that, in general, none of the methods was superior to the others in terms of predictive accuracy. In this paper we identify an inefficiency in the MDL approach as implemented by Kearns et al. and present an extended empirical evaluation by including a revised version of the MDL method and another approach based on the Minimum Message Length (MML) principle.
Original language | English |
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Title of host publication | Advanced Topics in Artificial Intelligence - 12th Australian Joint Conference on Artificial Intelligence, AI 1999, Proceedings |
Editors | Norman Foo |
Publisher | Springer |
Pages | 405-416 |
Number of pages | 12 |
ISBN (Print) | 3540668225, 9783540668220 |
DOIs | |
Publication status | Published - 1999 |
Event | Australasian Joint Conference on Artificial Intelligence 1999 - Sydney, Australia Duration: 6 Dec 1999 → 10 Dec 1999 Conference number: 12th https://link.springer.com/book/10.1007/3-540-46695-9 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 1747 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Australasian Joint Conference on Artificial Intelligence 1999 |
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Abbreviated title | AI 1999 |
Country/Territory | Australia |
City | Sydney |
Period | 6/12/99 → 10/12/99 |
Internet address |
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