Learning changing concepts by exploiting the structure of change

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Learning changing concepts by exploiting the structure of change

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Annual Workshop on Computational Learning Theory archive
Proceedings of the ninth annual conference on Computational learning theory table of contents

Desenzano del Garda, Italy

Pages: 131 - 139

Year of Publication: 1996

ISBN:0-89791-811-8

Authors

Peter L. Bartlett	Dept. of Systems Engineering, Australian National University, Canberra, ACT 0200, Australia
Shai Ben-David	Dept. of Computer Science, Technion, Haifa 32000, Israel
Sanjeev R. Kulkarni	Dept. of Electrical Engineering, Princeton University, Princeton, NJ

Sponsors

Univ degli Studi de Milano : Universite degli Studi de Milano
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 0, Downloads (12 Months): 7, Citation Count: 7

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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.

	1	Avrim Blum , Prasad Chalasani, Learning switching concepts, Proceedings of the fifth annual workshop on Computational learning theory, p.231-242, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130411]
	2	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
	3	A. J. Connolly, J. F. Chicharo, and P. Wilbers. Temperature modelling and prediction of steel strip in a HSM. In Control 92 Conference Proceedings. Institute of Engineers Australia, 1992.
	4	Torben Hagerup , C. Rüb, A guided tour of Chernoff bounds, Information Processing Letters, v.33 n.6, p.305-308, February 1990 [doi>10.1016/0020-0190(90)90214-I]
	5	David Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Information and Computation, v.100 n.1, p.78-150, Sept. 1992 [doi>10.1016/0890-5401(92)90010-D]
	6	David P. Helmbold , Philip M. Long, TRACKING DRIFTING CONCEPTS BY MINIMIZING DISAGREEMENTS, University of California at Santa Cruz, Santa Cruz, CA, 1991
	7	N. Merhav and M. Feder. Universal schemes for sequential decision from individual data sequences. IEEE Transactions on Information Theory, 39(4):1280-1292, 1993.
	8	Vladimir Vapnik, Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics), Springer-Verlag New York, Inc., Secaucus, NJ, 1982

CITED BY 7

	Dwi H. Widyantoro , Thomas R. Ioerger , John Yen, Tracking changes in user interests with a few relevance judgments, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA
	John Case , Sanjay Jain , Susanne Kaufmann , Arun Sharma , Frank Stephan, Predictive learning models for concept drift, Theoretical Computer Science, v.268 n.2, p.323-349, 17 October 2001
	Daniil Ryabko, Pattern Recognition for Conditionally Independent Data, The Journal of Machine Learning Research, 7, p.645-664, 12/1/2006
	Philip M. Long, The complexity of learning according to two models of a drifting environment, Proceedings of the eleventh annual conference on Computational learning theory, p.116-125, July 24-26, 1998, Madison, Wisconsin, United States
	Dwi H. Widyantoro , John Yen, Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data, IEEE Transactions on Knowledge and Data Engineering, v.17 n.3, p.401-412, March 2005
	Philip M. Long, The Complexity of Learning According to Two Models of a Drifting Environment, Machine Learning, v.37 n.3, p.337-354, Dec. 1999
	O. L. Perevozchikova , V. G. Tul'Chinskii , A. V. Kharchenko, Distinctive Features of Minimization of a Risk Functional in Mass Data Sets, Cybernetics and Systems Analysis, v.39 n.4, p.501-508, July-August 2003