An objective function based on Bayesian likelihoods of necessity and sufficiency for concept learning in the absence of labeled counter-examples

Skabar, Andrew 2004, An objective function based on Bayesian likelihoods of necessity and sufficiency for concept learning in the absence of labeled counter-examples, in IC-AI & MLMTA 2004 : Proceedings of the International Conference on Artificial Intelligence & Proceedings of the International Conference on Machine Learning : Models, Technologies & Applications, CSREA Press, [Las Vegas, Nev.], pp. 634-640.

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Title An objective function based on Bayesian likelihoods of necessity and sufficiency for concept learning in the absence of labeled counter-examples
Author(s) Skabar, Andrew
Conference name International Conference on Artificial Intelligence and International conference on Machine Learning; Models, Technologies and Applications (2004 : Las Vegas, Nev.)
Conference location Las Vegas, Nev.
Conference dates 21-24 Jun. 2004
Title of proceedings IC-AI & MLMTA 2004 : Proceedings of the International Conference on Artificial Intelligence & Proceedings of the International Conference on Machine Learning : Models, Technologies & Applications
Editor(s) Arabnia, Hamid
Mun, Youngson
Publication date 2004
Start page 634
End page 640
Publisher CSREA Press
Place of publication [Las Vegas, Nev.]
Keyword(s) concept learning
genetic algorithms
semi-supervised learning
Summary Supervised machine learning techniques generally require that the training set on which learning is based contain sufficient examples representative of the target concept, as well as known counter-examples of the concept; however, in many application domains it is not possible to supply a set of labeled counter-examples. This paper proposes an objective function based on Bayesian likelihoods of necessity and sufficiency. This function can be used to guide search towards the discovery of a concept description given only a set of labeled positive examples of the target concept, and as a corpus of unlabeled examples. Results of experiments performed on several datasets from the VCI repository show that the technique achieves comparable accuracy to conventional supervised learning techniques, despite the fact that the latter require a set of labeled counter-examples to be supplied. The technique can be applied in many domains in which the provision of labeled counter-examples is problematic.
ISBN 1932415327
9781932415322
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2004, CSREA Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005285

Document type: Conference Paper
Collection: School of Information Technology
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