Learning fixed-dimension linear thresholds from fragmented data

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Learning fixed-dimension linear thresholds from fragmented data

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

Santa Cruz, California, United States

Pages: 88 - 99

Year of Publication: 1999

ISBN:1-58113-167-4

Author

Paul W. Goldberg

Dept. of Computer Science, University of Warwick, Coventry CV4 7AL, U.K.

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Univ. of California, : University of California at Santa Cruz

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ACM New York, NY, USA

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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	Martin Anthony , Norman Biggs, Computational learning theory: an introduction, Cambridge University Press, New York, NY, 1992
	2	P.L. Bartlett, S.R. Kulkarni and S.E. Posner (1997). Covering numbers for real-valued function classes, IEEE Transactions on Information Theory 43(5), 1721- 1724.
	3	Shai Ben-David , Eli Dichterman, Learning with restricted focus of attention, Proceedings of the sixth annual conference on Computational learning theory, p.287-296, July 26-28, 1993, Santa Cruz, California, United States [doi>10.1145/168304.168353]
	4	Shai Ben-David , Eli Dichterman, Learnability with Restricted Focus of Attention guarantees Noise-Tolerance, Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory, p.248-259, October 10-15, 1994
	5	Shai Ben-David , Alon Itai , Eyal Kushilevitz, Learning by distances, Information and Computation, v.117 n.2, p.240-250, March 1995 [doi>10.1006/inco.1995.1042]
	6	Andreas Birkendorf , Eli Dichterman , Jeffrey Jackson , Norbert Klasner , Hans Ulrich Simon, On Restricted-Focus-of-Attention Learnability of Boolean Functions, Machine Learning, v.30 n.1, p.89-123, Jan. 1998 [doi>10.1023/A:1007458528570]
	7	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]
	8	Avrim Blum , Prasad Chalasani , Sally A. Goldman , Donna K. Slonim, Learning with unreliable boundary queries, Journal of Computer and System Sciences, v.56 n.2, p.209-222, April 1998 [doi>10.1006/jcss.1997.1559]
	9	A. Blum , A. Frieze, A polynomial-time algorithm for learning noisy linear threshold functions, Proceedings of the 37th Annual Symposium on Foundations of Computer Science, p.330, October 14-16, 1996
	10	J. O'Brien (1998). An Algorithm for the Fusion of Correlated Probabilities. Procs. of FUSION'98, Las Vegas, July 1998.
	11	1. Bruck (1990). Harmonic analysis of polynomial threshold functions. SIAM Journal of Discrete Mathematics, 3 (2), 168-177.
	12	C.K. Chow (1961). On the characterisation of threshold functions. Proc. Syrup. on Switching Circuit Theory and Logical Design, 34-38.
	13	K. Copsey (1998). A Discounting Method for Reducing the Effect of Distribution Variability in Probabilitylevel Data Fusion. Procs. of EuroFusion'98, Great Malvern, UK, Oct. 1998.
	14	Dempster, Laird and Rubin (1977). Maximum Likelihood from Incomplete Data via the EM Alorithm, J. Roy. Stat. Soc. 39(1), 1-38.
	15	E. Dichterman (1998). Learning with Limited Visibility. CDAM Research Reports Series, LSE-CDAM-98- O1 44pp.
	16	M. E. Dyer, A. M. Frieze, R. Kannan, A. Kapoor, L. Perkovic and U. Vazirani (1993). A mildly exponential time algorithm for approximating the number of solutions to a multidimensional knapsack problem. Combinatorics, Probability and Computing 2, 271-284.
	17	Sally A. Goldman , Stephen S. Kwek , Stephen D. Scott, Learning from examples with unspecified attribute values (extended abstract), Proceedings of the tenth annual conference on Computational learning theory, p.231-242, July 06-09, 1997, Nashville, Tennessee, United States [doi>10.1145/267460.267504]
	18	S.A. Goldman and R.H. Sloan (1995). Can PAC Learning Algorithms Tolerate Random Attribute Noise? Algorithmica 14, 70-84.
	19	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]
	20	Michael J. Kearns , Robert E. Schapire, Efficient distribution-free learning of probabilistic concepts, Journal of Computer and System Sciences, v.48 n.3, p.464-497, June 1994 [doi>10.1016/S0022-0000(05)80062-5]
	21	Michael Kearns , Yishay Mansour , Dana Ron , Ronitt Rubinfeld , Robert E. Schapire , Linda Sellie, On the learnability of discrete distributions, Proceedings of the twenty-sixth annual ACM symposium on Theory of computing, p.273-282, May 23-25, 1994, Montreal, Quebec, Canada [doi>10.1145/195058.195155]
	22	Roderick J A Little , Donald B Rubin, Statistical analysis with missing data, John Wiley & Sons, Inc., New York, NY, 1986
	23	D. Lowe and A.R. Webb (1990). Exploiting prior knowledge in network optimization: an illustration from medical prognosis, Network 1,299-323.
	24	D. Pollard (1984). Convergence of Stochastic Processes, Springer-Verlag, Berlin/New York.
	25	Robert Sloan, Types of noise in data for concept learning, Proceedings of the first annual workshop on Computational learning theory, p.91-96, August 03-05, 1988, MIT, Cambridge, Massachusetts, United States
	26	D.M. Titterington, G.D. Murray, L.S. Murray, D.J. Spiegelhalter, A.M. Skene, J.D.F. Habbena and G.J.Gelpke (1981). Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients, J. Roy. Stat. Soc. 144, 145-175.