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.

	Ang88	Dana Angluin, Queries and Concept Learning, Machine Learning, v.2 n.4, p.319-342, April 1988  [doi>10.1023/A:1022821128753]
	AW98	Peter Auer , Manfred K. Warmuth, Tracking the Best Disjunction, Machine Learning, v.32 n.2, p.127-150, Aug. 1998  [doi>10.1023/A:1007472513967 ]
	Blo62	H.D. Block. The perceptron: A model for brain functioning. Reviews of Modern Physics, 34:123-135, 1962. Reprinted in Neurocomputing by Anderson and Rosenfeld.
	Bre67	L.M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Physics, 7:200-217, 1967.
	Byl97	Tom Bylander, The binary exponentiated gradient algorithm for learning linear functions, Proceedings of the tenth annual conference on Computational learning theory, p.184-192, July 06-09, 1997, Nashville, Tennessee, United States  [doi>10.1145/267460.267495]
	CBFHW96	Nicolò Cesa-Bianchi , Yoav Freund , David P. Helmbold , Manfred K. Warmuth, On-line prediction and conversion strategies, Machine Learning, v.25 n.1, p.71-110, Oct. 1996  [doi>10.1023/A:1018348209754 ]
	CBFH+97	Nicolò Cesa-Bianchi , Yoav Freund , David Haussler , David P. Helmbold , Robert E. Schapire , Manfred K. Warmuth, How to use expert advice, Journal of the ACM (JACM), v.44 n.3, p.427-485, May 1997  [doi>10.1145/258128.258179]
	CBHP98	N. Cesa-Bianchi, P.H. Helmbold, and S. Panizza. On bayes methods for online boolean prediction. Algorithmica, 22 (1):112-137, 1998.
	CBLW96	N. Cesa-Bianchi and P. Long and M. K. Warmuth, Worst-case quadratic loss bounds for on-line prediction of linear functions by gradient descent, IEEE Transactions on Neural Networks, 7:604-619, 1996.
	CL81	Y. Censor and A. Lent. An iterative rowaction method for interval convex programming. Journal of Optimization Theory and Applications, 34(3):321-353, July 1981.
	FS98	Yoav Freund , Robert E. Schapire, Large margin classification using the perceptron algorithm, Proceedings of the eleventh annual conference on Computational learning theory, p.209-217, July 24-26, 1998, Madison, Wisconsin, United States  [doi>10.1145/279943.279985]
	GW98	Claudio Gentile , Manfred K. Warmuth, Linear hinge loss and average margin, Proceedings of the 1998 conference on Advances in neural information processing systems II, p.225-231, July 1999
	GLS97	Adam J. Grove , Nick Littlestone , Dale Schuurmans, General convergence results for linear discriminant updates, Proceedings of the tenth annual conference on Computational learning theory, p.171-183, July 06-09, 1997, Nashville, Tennessee, United States  [doi>10.1145/267460.267493]
	HKW95	D.P. Helmbold, J. Kivinen, and M. K. Warmuth. '~brst-case ioss bounds for sigmoided linear neurons. In Proc. 1995 Neural Information Processing Conference, pages 309-3 !5. MIT Press, Cambridge, MA, November 1995.
	HW98	Mark Herbster , Manfred K. Warmuth, Tracking the best regressor, Proceedings of the eleventh annual conference on Computational learning theory, p.24-31, July 24-26, 1998, Madison, Wisconsin, United States  [doi>10.1145/279943.279949]
	JW98	A. Jagota and M. K. Warmuth. Continuous and discrete time nonlinear gradient descent: relative loss bounds and convergence. In R. Greiner E. Boros, editor, Electronic Proceedings of Fifth international Symposium on Artificial Intelligence and Mathematics: Electronie,http:/!mtcor.rutgers.eduFamai, 1998.
	KW97	J. Kivinen and M. K. Warmuth. Additive versus exponentiated gradient updates for linear prediction. Information and Computation, 1~t'")/1~,-1 AA Tnfil.n~, 1OO"7 I dJ.d~ 1}. I~"T, OflllU,(~JLl{J IdJ 1.
	KW98b	Jyrki Kivinen , Manfred K. Warmuth, Relative loss bounds for multidimensional regression problems, Proceedings of the 1997 conference on Advances in neural information processing systems 10, p.287-293, July 1998, Denver, Colorado, United States
	KW98c	Jyrki Kivinen , Manfred K. Warmuth, Averaging Expert Predictions, Proceedings of the 4th European Conference on Computational Learning Theory, p.153-167, March 29-31, 1999
	Lit88	Nick Littlestone, Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm, Machine Learning, v.2 n.4, p.285-318, April 1988  [doi>10.1023/A:1022869011914]
	Lit89	N. Littlestone, Mistake bounds and logarithmic linear-threshold learning algorithms, University of California at Santa Cruz, Santa Cruz, CA, 1990
	Lit91	Nicholas Littlestone, Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow, Proceedings of the fourth annual workshop on Computational learning theory, p.147-156, August 05-07, 1991, Santa Cruz, California, United States
	LW94	Nick Littlestone , Manfred K. Warmuth, The weighted majority algorithm, Information and Computation, v.108 n.2, p.212-261, Feb. 1, 1994  [doi>10.1006/inco.1994.1009 ]
	LM97	N. Littlestone and C. Mesterharm, An Apobayesian Relative of Winnow, Advances in Neural Information Processing Systems, IVlOI'~illl I'~UlIII~I. IIII, l~/.
	LM99	N. Littlestone and C. Mesterhaml, A Simulation Study of Winnow and Related Learning Algorithms (unpublished manuscript), 1999.
	Nov62	A.B.J. Novikov. On convergence proofs on perceptrons, in Proc. of the Symposium on the Mathematical Theory of Automata, vol. XII, pages 615-622,1962.
	Roc70	R. Rockafellar. Convex Analysis, Princeton University press, 1970.
	Ros62	F. Rosenblatt. Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books, Washington, D.C., 1962.
	Vov97	V. Vovk, Competitive on-line linear regression, Tech. Rep. Department of Computer Science, Royal Holloway, University of London, CSD-TR-97-13, 1997, http://www, cs.rhbnc.ac.uk/research/compint /areas/comp-learn/aa/nips.ps
	Vov90	Volodimir G. Vovk, Aggregating strategies, Proceedings of the third annual workshop on Computational learning theory, p.371-386, August 06-08, 1990, Rochester, New York, United States
	WH60	B. Widrow and M. E. Hoff, Adaptive switching circuits. In 1960 IRE WESCON Cony. Record, Part 4, pages 96-104, 1960_
	Yam98	K. Yamanishi, A decision-theoretic extension of stochastic complexity and its applications to learning. IEEE Information Theory, 44(4): 1424-1439.

• Data structures for quadtree approximation and compression Communications of the ACM   28, 9
  Hanan Samet
  
  
• A hierarchical single-key-lock access control using the Chinese remainder theorem Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing
  Kim S. Lee ,  Huizhu Lu ,  D. D. Fisher
  
  
• The GemStone object database management system Communications of the ACM   34, 10
  Paul Butterworth ,  Allen Otis ,  Jacob Stein
  
  
• Putting innovation to work: adoption strategies for multimedia communication systems Communications of the ACM   34, 12
  Ellen Francik ,  Susan Ehrlich Rudman ,  Donna Cooper ,  Stephen Levine
  
  
• An intelligent component database for behavioral synthesis Proceedings of the 27th ACM/IEEE conference on Design automation
  Gwo-Dong Chen ,  Daniel D. Gajski