On-line learning of functions of bounded variation under various sampling schemes

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On-line learning of functions of bounded variation under various sampling schemes

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

Santa Cruz, California, United States

Pages: 439 - 445

Year of Publication: 1993

ISBN:0-89791-611-5

Authors

S. E. Posner
S. R. Kulkarni

Sponsors

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): 11, Downloads (12 Months): 16, Citation Count: 2

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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.

	Hau92	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]
	HB92	David Haussler , A. Barron, HOW WELL DO BAYES METHODS WORK FOR ON-LINE PREDICTION OF {+- 1} VALUES?, University of California at Santa Cruz, Santa Cruz, CA, 1992
	KL92	Don Kimber , Philip M. Long, The learning complexity of smooth functions of a single variable, Proceedings of the fifth annual workshop on Computational learning theory, p.153-159, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130402]
	Kul91	S.R. Kullmrni. A review of some extensions to the pax: learning model. Workahop on Lea~ing and Geometr'g, 1991.
	LW89	N. Littlestone and M.K. Warmuth. The weighted majority algorithm. In P,oceedings of 30th Annual $~rap. on Fovnd. of Co~p. $ci., 1989.
	Myc88	J. Mycielski. A learning algorithm for linear operators. In Proceedings of the Araerican Mathematical Society, volume 103(2), pages 547-550, 1988.
	Poo88	H. Vincent Poor, An introduction to signal detection and estimation (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1994

CITED BY 2

	Philip M. Long, On the sample complexity of learning functions with bounded variation, Proceedings of the eleventh annual conference on Computational learning theory, p.126-133, July 24-26, 1998, Madison, Wisconsin, United States
	Philip M. Long, Efficient algorithms for learning functions with bounded variation, Information and Computation, v.188 n.1, p.99-115, 10 January 2004