Rough sets and higher order vagueness

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3641 LNAI, Issue , 2005, Pages 33-42

  Skowron, Andrzej ^a   Swiniarski, Roman ^b,c  

^a UNIVERSITY OF WARSAW   (Poland)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

^c SAN DIEGO STATE UNIVERSITY   (United States)

Author keywords

Adaptive learning; Higher order vagueness; Rough sets; Vagueness

Indexed keywords

ADAPTIVE SYSTEMS; APPROXIMATION THEORY; BOUNDARY CONDITIONS; LEARNING SYSTEMS;

ADAPTIVE LEARNING; HIGHER ORDER VAGUENESS; ROUGH SETS; VAGUENESS;

SET THEORY;

EID: 33645961610     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11548669_4     Document Type: Conference Paper

References (21)

1. J. Friedman, T. Hastie, R. Tibshirani (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, Heidelberg.
2. R. Keefe (2000). Theories of Vagueness. Cambridge Studies in Philosophy, Cambridge, UK.
3. J. Pavelka (1979). On Fuzzy Logic I-III. Zeit. Math Logik Grund. Math. 25, 45-52, 119-134, 447-464.
4. Z. Pawlak (1991). Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving 9, Kluwer Academic Publishers, Dordrecht.
5. Z. Pawlak, A. Skowron (1994). Rough Membership Functions. In R. R. Yager, M. Fedrizzi, J. Kacprzyk (eds.), Advances in the Dempster-Schafer Theory of Evidence, John Wiley and Sons, New York, 251-271.
6. L. Polkowski (2002). Rough Sets: Mathematical Foundations. Physica-Verlag, Heidelberg.
7. L. Polkowski, A. Skowron (1996). Rough Mereology: A New Paradigm for Approximate Reasoning. International Journal of Approximate Reasoning 15, 333-365.
8. S. Read (1995). Thinking about Logic. An Introduction to the Philosophy of Logic. Oxford University Press, Oxford, New York.
9. A. Skowron (2000) Rough Sets in KDD. 16-th World Computer Congress (IFIP'2000), Beijing, August 19-25, 2000, In: Zhongzhi Shi, Boi Faltings, Mark Musen (Eds.) Proceedings of Conference on Intelligent Information Processing (IIP2000), Publishing House of Electronic Industry, Beijing, 2000, 1-17.
10. A. Skowron (2005). Rough Sets and Vague Concepts. Fundamenta Informaticae 4(1-4), 417-431.
11. A. Skowron, J. Stepaniuk (1996). Tolerance Approximation Spaces. Fundamenta Informaticae 27, 245-253.
12. A. Skowron, J. Stepaniuk (2004). Information Granules and Rough-Neural Computing. In S.K. Pal, L. Polkowski, A. Skowron (eds.), Rough-Neural Computing: Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag, Heidelberg, 43-84.
13. A. Skowron, R. Swiniarski, P. Synak (2004). Approximation Spaces and Information Granulation. LNAI 3066, Springer, Heidelberg, 114-123.
14. P. Stone (2000). Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge, MA.
15. Z. Suraj (1998). The synthesis problem of concurrent systems specified by dynamic information systems. In Polkowski, L., Skowron, A. (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, 418-448.
16. R.S. Sutton, A.G. Barto (1998). Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA.
17. V. Vapnik (1998). Statistical Learning Theory. John Wiley & Sons, New York, NY.
18. A. Wojna (2001). Constraint Based Incremental Learning of Classification Rules. LNAI 2005, 428-435, Springer-Verlag, Heidelberg.
19. W. Ziarko (1993). Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39-59.
20. L.A. Zadeh (1965). Fuzzy sets. Information and Control 8, 333-353.
21. L.A. Zadeh (1996). Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems 2, 103-111.