Incremental rule learning based on example nearness from numerical data streams

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2005, Pages 568-572

  Ferrer Troyano, Francisco ^a   Aguilar Ruiz, Jesus S ^a   Riquelme, Jose C ^a  

^a Dept of Computer Science ^*  (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

HEURISTIC METHODS; LEARNING SYSTEMS; ONLINE SYSTEMS;

CLASSIFICATION SYSTEM; DATA STREAMS; INCREMENTAL LEARNING;

DATA MINING;

EID: 33644551188     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1066677.1066808     Document Type: Conference Paper

References (20)

1. th ACM Symposium on Applied Computing - SAC'04, pages 649-653.
2. th ACM Symposium on Applied Computing - SAC'04, pages 632-636.
3. th Int. Conf. on Very Large Data Bases - VLDB'98, pages 416-427, 1998.
4. L. Golab and M. Ozsu. Issues in data stream management. SIGMOD Record, 32(2):5-14, 2003.
5. M. Harries, C. Sammut, and K. Horn. Extracting hidden context. Machine Learning, 32(2):101-126, 1998.
6. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining - KDD'01, pages 97-106.
7. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining - KDD'03.
8. R. Klinkenberg. Learning drifting concepts: example selection vs. example weighting. Intelligent Data Analysis, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, 8(3), 2004.
9. th IEEE Int. Conf. on Data Mining - ICDM'03, pages 123-130, 2003.
10. M. Lazarescu, S. Venkatesh, and H. Bui. Using multiple windows to track concept drift. Technical report, Faculty of Computer Science, Curtin University, 2003.
11. N. Littlestone and M. Warmuth. The weighted majority algorithm. Information and Computation, 108:212-261, 1994.
12. th IEEE Int. Joint Conf. on Neural Networks IJCNN'03, pages 2764-2769, 2003.
13. M. Maloof and R. Michalski. Incremental learning with partial instance memory. Artificial Intelligence, 154:95-126, 2004.
14. M. Salganicoff. Tolerating concept and sampling shift in lazy learning using prediction error context switching. AI Review, Special Issue on Lazy Learning, 11(1-5):133-155, 1997.
15. J. Schlimmer and R. Granger. Incremental learning from noisy data. Machine Learning, 1(3):317-354, 1986.
16. th Int. Conf. on Very Large Databases VLDB'96, pages 544-555, 1996.
17. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining - KDD'01, pages 377-382.
18. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining - KDD'99, pages 272-276. ACM Press, 1999.
19. th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining KDD'03, pages 226-235.
20. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1):69-101, 1996.