On-line Algorithms in Machine Learning

Abstract : The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas differ in terms of their emphasis and the problems typically studied, there are a collection of results in Computational Learning Theory that fit nicely into the 'on-line algorithms' framework. This survey article discusses some of the results, models, and open problems from Computational Learning Theory that seem particularly interesting from the point of view of on-line algorithms research. The emphasis in this article is on describing some of the simpler, more intuitive results, whose proofs can be given in their entirety. Pointers to the literature are given for more sophisticated versions of these algorithms.

[1]  D. Blackwell.  An analog of the minimax theorem for vector payoffs. , 1956 .

[2]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[3]  H. Robbins.  Asymptotically Subminimax Solutions of Compound Statistical Decision Problems , 1985 .

[4]  Ronald L. Rivest,et al.  Learning decision lists , 2004, Machine Learning.

[5]  Leslie G. Valiant,et al.  On the learnability of Boolean formulae , 1987, STOC.

[6]  N. Littlestone.  Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[7]  Alfredo De Santis,et al.  Learning probabilistic prediction functions , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.

[8]  Vladimir Vovk,et al.  Aggregating strategies , 1990, COLT '90.

[9]  Avrim Blum.  Learning boolean functions in an infinite attribute space , 1990, STOC '90.

[10]  Avrim Blum,et al.  Separating distribution-free and mistake-bound learning models over the Boolean domain , 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

[11]  Neil D. Pearson,et al.  Consumption and Portfolio Policies With Incomplete Markets and Short‐Sale Constraints: the Finite‐Dimensional Case , 1991 .

[12]  Lisa Hellerstein,et al.  Learning in the presence of finitely or infinitely many irrelevant attributes , 1991, COLT '91.

[13]  Nick Littlestone,et al.  Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow , 1991, COLT '91.

[14]  Philip M. Long,et al.  On-line learning of linear functions , 1991, STOC '91.

[15]  R. Schapire.  Toward Eecient Agnostic Learning , 1992 .

[16]  Neri Merhav,et al.  Universal sequential learning and decision from individual data sequences , 1992, COLT '92.

[17]  Neri Merhav,et al.  Universal prediction of individual sequences , 1992, IEEE Trans. Inf. Theory.

[18]  Linda Sellie,et al.  Toward efficient agnostic learning , 1992, COLT '92.

[19]  David Haussler,et al.  How to use expert advice , 1993, STOC.

[20]  Dean P. Foster,et al.  A Randomization Rule for Selecting Forecasts , 1993, Oper. Res..

[21]  Michael Kearns,et al.  Efficient noise-tolerant learning from statistical queries , 1993, STOC.

[22]  Manfred K. Warmuth,et al.  The Weighted Majority Algorithm , 1994, Inf. Comput..

[23]  Avrim Blum,et al.  Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain , 1995, ICML.

[24]  Thorsten Joachims,et al.  WebWatcher : A Learning Apprentice for the World Wide Web , 1995 .

[25]  Vladimir Vovk,et al.  A game of prediction with expert advice , 1995, COLT '95.

[26]  Erik Ordentlich,et al.  On-line portfolio selection , 1996, COLT '96.

[27]  Erik Ordentlich,et al.  Universal portfolios with side information , 1996, IEEE Trans. Inf. Theory.

[28]  T. Cover.  Universal Portfolios , 1996 .

[29]  Yoav Freund,et al.  Game theory, on-line prediction and boosting , 1996, COLT '96.

[30]  Yoav Freund,et al.  Predicting a binary sequence almost as well as the optimal biased coin , 2003, COLT '96.

[31]  Avrim Blum,et al.  On-line Learning and the Metrical Task System Problem , 1997, COLT '97.

[32]  M. Kearns.  Efficient noise-tolerant learning from statistical queries , 1998, JACM.

[33]  Gerhard J. Woeginger,et al.  Developments from a June 1996 seminar on Online algorithms: the state of the art , 1998 .

[34]  Manfred K. Warmuth,et al.  Learning nested differences of intersection-closed concept classes , 2004, Machine Learning.