Abstract
From the classic view in which a concept consists of the set of extents and the set of intents, a concept learning system extended from a formal context is introduced and two concepts such as an under concept and an over concept are defined. Any pair of subsets from extents and intents in this concept learning system can be changed to an under or an over concept. Further it can be changed to a concept by learning from the set of extents or from the set of intents. It is proved that the concept learned in this framework is an optimal concept. This process of learning a concept describes the recognizing ability from unclear to clear.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Glymour, C.: The hierarchies of knowledge and the mathematics of discovery. Minds and Machines 1, 75–95 (1991)
Kelly, K.: The Logic of Reliable Inquiry. Oxford University Press, Oxford (1996)
Merrill, M., Tennyson, R.: Concept Teaching: An Instructional Design Guide. Educational Technology, Englewood Cliffs (1977)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Peikoff, L.: Objectivism: The Philosophy of Ayn Rand. Dutton, New York (1991)
Qiu, G.F., Li, H.Z., et al.: A knowledge processing method for intelligent systems based on inclusion degree. Expert Systems 20(4), 187–195 (2003)
Schulte, O.: Means-ends epistemology. The British Journal for the Philosophy of Science 50, 1–31 (1999)
Skowron, A.: The rough set theory and evidence theory. Fundamenta Informatica XII, 245–262 (1990)
Smith, E.: Concepts and induction. In: Posner, M. (ed.) Foundations of Cognitive Science, pp. 501–526. MIT Press, Cambridge (1989)
Sowa, J.: Conceptual Structures, Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)
Tennyson, R., Cocchiarella, M.: An empirically based instructional design theory for teaching concepts. Review of Educational Research 56(1), 40–71 (1986)
Van Mechelen, I., Hampton, J., et al. (eds.): Categories and Concepts, Theoretical Views and Inductive Data Analysis. Academic Press, New York (1993)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, Reidel, Dordrecht (1982)
Yao, Y.Y.: Concept Lattices in Rough Set Theory. In: Dick, S., Kurgan, L., et al. (eds.) Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), June 27-30, 2004, pp. 796–801. IEEE Catalog Number: 04TH8736 (2004)
Yao, Y.Y.: Constructive and algerbraic methods of the theory of rough sets. Information Sciences 109, 21–47 (1998)
Zhang, W.X., Wei, L., et al.: Attribute reduction in concept lattice based on discernibility matrix. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 157–165. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Qiu, GF. (2007). Learning Models Based on Formal Concept. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-72458-2_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
eBook Packages: Computer ScienceComputer Science (R0)