Handling concept drifts in incremental learning with support vector machines

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Handling concept drifts in incremental learning with support vector machines

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Diego, California, United States

Pages: 317 - 321

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Nadeem Ahmed Syed	Program for Research in Intelligent Systems (PRIS), School of Computing, National University of Singapore, Singapore 119260
Huan Liu	Program for Research in Intelligent Systems (PRIS), School of Computing, National University of Singapore, Singapore 119260
Kah Kay Sung	Program for Research in Intelligent Systems (PRIS), School of Computing, National University of Singapore, Singapore 119260

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 10, Downloads (12 Months): 114, Citation Count: 9

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 9

	Marcus A. Maloof , Ryszard S. Michalski, Incremental learning with partial instance memory, Artificial Intelligence, v.154 n.1-2, p.95-126, April 2004
	Francisco Ferrer-Troyano , Jesus S. Aguilar-Ruiz , Jose C. Riquelme, Incremental rule learning based on example nearness from numerical data streams, Proceedings of the 2005 ACM symposium on Applied computing, March 13-17, 2005, Santa Fe, New Mexico
	Ralf Klinkenberg, Learning drifting concepts: Example selection vs. example weighting, Intelligent Data Analysis, v.8 n.3, p.281-300, August 2004
	Francisco Ferrer-Troyano , Jesus S. Aguilar-Ruiz , Jose C. Riquelme, Data streams classification by incremental rule learning with parameterized generalization, Proceedings of the 2006 ACM symposium on Applied computing, April 23-27, 2006, Dijon, France
	F. Fdez-Riverola , E. L. Iglesias , F. Díaz , J. R. Méndez , J. M. Corchado, Applying lazy learning algorithms to tackle concept drift in spam filtering, Expert Systems with Applications: An International Journal, v.33 n.1, p.36-48, July, 2007
	Daniele Loiacono , Andrea Marelli , Pier Luca Lanzi, Support vector regression for classifier prediction, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England
	Claudia Diamantini , Maurizio Panti, An efficient and scalable data compression approach to classification, ACM SIGKDD Explorations Newsletter, v.2 n.2, p.49-55, Dec. 2000
	Huan Liu , Hiroshi Motoda, On Issues of Instance Selection, Data Mining and Knowledge Discovery, v.6 n.2, p.115-130, April 2002
	J. Zico Kolter , Marcus A. Maloof, Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts, The Journal of Machine Learning Research, 8, p.2755-2790, 12/1/2007