Learning internal representations

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Learning internal representations

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

Santa Cruz, California, United States

Pages: 311 - 320

Year of Publication: 1995

ISBN:0-89791-723-5

Author

Jonathan Baxter

Department of Mathematics and Statistics, The Flinders University of South Australia

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 19, Downloads (12 Months): 54, Citation Count: 11

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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	J. Baxter. Learning Inter, al Representaiwn.s. PhD thesis, Department of Mathematics and Statistics, The Flinders University of South Australia, 1995. Draft copy in Neuroprose Archive under "/pub /neuroprose /Thesis/baxter.thesis.ps.Z' .
	2	Stuart Geman , Elie Bienenstock , René Doursat, Neural networks and the bias/variance dilemma, Neural Computation, v.4 n.1, p.1-58, Jan. 1992 [doi>10.1162/neco.1992.4.1.1]
	3	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]
	4	D. Pollard. Convergence of Stochastic Processes. Springer-Verlag, New' York, 1984.
	5	D. Rumelhart, G. Hinton. and R. Williams. Learning representations by back-propagating errors. Yalure, 323:533-536, 1986.

CITED BY 11

	Jonathan Baxter, A Bayesian/information theoretic model of bias learning, Proceedings of the ninth annual conference on Computational learning theory, p.77-88, June 28-July 01, 1996, Desenzano del Garda, Italy
	Tony Jebara, Multi-task feature and kernel selection for SVMs, Proceedings of the twenty-first international conference on Machine learning, p.55, July 04-08, 2004, Banff, Alberta, Canada
	Neil D. Lawrence , John C. Platt, Learning to learn with the informative vector machine, Proceedings of the twenty-first international conference on Machine learning, p.65, July 04-08, 2004, Banff, Alberta, Canada
	Ya Xue , Xuejun Liao , Lawrence Carin , Balaji Krishnapuram, Multi-Task Learning for Classification with Dirichlet Process Priors, The Journal of Machine Learning Research, 8, p.35-63, 5/1/2007
	Jonathan Baxter, A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling, Machine Learning, v.28 n.1, p.7-39, July 1997
	Rich Caruana, Multitask Learning, Machine Learning, v.28 n.1, p.41-75, July 1997
	Rich Caruana , Virginia R. de Sa, Benefitting from the variables that variable selection discards, The Journal of Machine Learning Research, 3, 3/1/2003
	Shai Ben-David , Reba Schuller Borbely, A notion of task relatedness yielding provable multiple-task learning guarantees, Machine Learning, v.73 n.3, p.273-287, December 2008
	Koby Crammer , Michael Kearns , Jennifer Wortman, Learning from Multiple Sources, The Journal of Machine Learning Research, 9, p.1757-1774, 6/1/2008
	Jonathan Baxter, A model of inductive bias learning, Journal of Artificial Intelligence Research, v.12 n.1, p.149-198, February 2000
	Shihao Ji , David Dunson , Lawrence Carin, Multitask compressive sensing, IEEE Transactions on Signal Processing, v.57 n.1, p.92-106, January 2009