Abstract
Let us now make the final conclusions concerning the learning process by examples based on the concepts of a possibility distribution function and a rough set. As was shown the interpretation JS has the power of reducing the uncertainty whether an element belongs or does not belong to a concept to be learned. This is an improvement over the interpretation MS which can not provide any information for elements in the boundary. It can be observed that the computed function GS gets closer to the interpretation JS as the number of the learned concepts grows larger. The measure of learning defined by us reflects the effect of arranging the set of concepts to be learned into a particular sequence. The optimization method that we have outlined can be used to improve the learning process.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Aikins, J., "A theory and methodology of inductive learning", Artificial Intelligence, Vol.20, No.3, 1983
Buchanan, B. and Duda, R., "Principles of rule-based expert systems", Advances in Computers, Vol.22, 1983
Bobrow, D. and Collins, A., Representation and understanding, Academic Press, New York, 1975
Dietterich, T. and Michalski, R., "Inductive learning of structural descriptions", Artificial Intelligence, Vol.16, No.3, 1981
Michalski, R., "A theory and methodology of inductive learning", Artificial Intelligence, Vol.20, 1983, 111–16
Michalski, R. and Chilausky, R., "Learning by being told and learning from examples", Internat. J. Policy Anal. Inform. Systems, Vol.4, No.2, 1980, 125–160
Pawlak, Z., "Rough sets", Internat. J.Comp. and Info. Sci., Vol.11, 1982, 341–366
Pawlak, Z., "Information systems — theoretical foundations", Information Systems, Vol.6, No.3, 1981, 205–218
Ras, Z., "An algebraic approach to information retrieval systems" Internat. J. Comp. and Info. Sci., Vol.11, 1982, 275–293
Ras, Z., Zemankova, M., "Rough sets based learning systems", Proc. of the Conference on Computation Theory in Zaborow, Poland, December 1984, Lecture Notes in Computer Science, Springer Verlag, No. 208, 265–275
Winston, P., "Learning structural descriptions from examples", MAC TR-76, MIT, September 1970
Winston, P., "Learning by augmenting rules and accumulating censors", Report AIM-678, Artificial Intelligence Laboratory MIT, 1982
Zemankova-Leech, M. and Kandel, A., "Uncertainty propagation to expert systems", Approximate Reasoning in Expert Systems, eds. Gupta, M., Kandel, A., Bandler, W. and Kiszka, North Holland Publishers, 1985 /to appear/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1986 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ras, Z.W., Zemankova, M. (1986). Learning in knowledge based systems, a possibilistic approach. In: Gruska, J., Rovan, B., Wiedermann, J. (eds) Mathematical Foundations of Computer Science 1986. MFCS 1986. Lecture Notes in Computer Science, vol 233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0016290
Download citation
DOI: https://doi.org/10.1007/BFb0016290
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-16783-9
Online ISBN: 978-3-540-39909-4
eBook Packages: Springer Book Archive