Abstract
Approximation spaces are fundamental for the rough set approach. We discuss their application in machine learning and pattern recognition.
Chapter PDF
Similar content being viewed by others
References
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2003)
Łukasiewicz, J.: Die logischen Grundlagen der Wahrscheinilchkeitsrechnung, Kraków 1913. In: Borkowski, L. (ed.) Jan Łukasiewicz - Selected Works. North Holland, Amsterdam, Polish Scientific Publishers, Warsaw (1970)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Polkowski, L.: Rough Sets: Mathematical Foundations. In: Advances in Soft Computing. Physica, Heidelberg (2002)
Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. Journal of Approximate Reasoning 15(4), 333–365 (1996)
Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)
Staab, S., Studer, R. (eds.): Handbook on Ontologies. International Handbooks on Information Systems. Springer, Heidelberg (2004)
Skowron, A., Synak, P.: Complex patterns. Fundamenta Informaticae 60(1-4), 351–366 (2004)
Skowron, A., Świniarski, R.W., Synak, P.: Approximation spaces and information granulation. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 175–189. Springer, Heidelberg (2005)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skowron, A., Stepaniuk, J., Swiniarski, R. (2005). Approximation Spaces in Machine Learning and Pattern Recognition. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_121
Download citation
DOI: https://doi.org/10.1007/11590316_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
Online ISBN: 978-3-540-32420-1
eBook Packages: Computer ScienceComputer Science (R0)