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Polynomial learnability of linear threshold approximations

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Tom BylanderAuthors Info & Claims

COLT '93: Proceedings of the sixth annual conference on Computational learning theory

Pages 297 - 302

https://doi.org/10.1145/168304.168356

Published: 01 August 1993 Publication History

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Kalai A(2004)Learning Monotonic Linear FunctionsLearning Theory10.1007/978-3-540-27819-1_34(487-501)Online publication date: 2004
https://doi.org/10.1007/978-3-540-27819-1_34
Blum ADunagan JEppstein D(2002)Smoothed analysis of the perceptron algorithm for linear programmingProceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms10.5555/545381.545499(905-914)Online publication date: 6-Jan-2002
https://dl.acm.org/doi/10.5555/545381.545499
Blum AFrieze AKannan RVempala S(1996)A polynomial-time algorithm for learning noisy linear threshold functionsProceedings of 37th Conference on Foundations of Computer Science10.1109/SFCS.1996.548492(330-338)Online publication date: 1996
https://doi.org/10.1109/SFCS.1996.548492
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Index Terms

Polynomial learnability of linear threshold approximations
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Models of computation
    1. Probabilistic computation

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COLT '93: Proceedings of the sixth annual conference on Computational learning theory

August 1993

463 pages

ISBN:0897916115

DOI:10.1145/168304

Editor:
Lenny Pitt
Univ. of Illinois, Urbana

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 01 August 1993

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6COLT93: 6th Annual Conference on Computational Learning Theory

July 26 - 28, 1993

California, Santa Cruz, USA

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Kalai A(2004)Learning Monotonic Linear FunctionsLearning Theory10.1007/978-3-540-27819-1_34(487-501)Online publication date: 2004
https://doi.org/10.1007/978-3-540-27819-1_34
Blum ADunagan JEppstein D(2002)Smoothed analysis of the perceptron algorithm for linear programmingProceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms10.5555/545381.545499(905-914)Online publication date: 6-Jan-2002
https://dl.acm.org/doi/10.5555/545381.545499
Blum AFrieze AKannan RVempala S(1996)A polynomial-time algorithm for learning noisy linear threshold functionsProceedings of 37th Conference on Foundations of Computer Science10.1109/SFCS.1996.548492(330-338)Online publication date: 1996
https://doi.org/10.1109/SFCS.1996.548492
Bylander TWarmuth M(1994)Learning linear threshold functions in the presence of classification noiseProceedings of the seventh annual conference on Computational learning theory10.1145/180139.181176(340-347)Online publication date: 16-Jul-1994
https://dl.acm.org/doi/10.1145/180139.181176
Bylander T(1994)Learning linear threshold functionsProceedings of IEEE International Conference on Systems, Man and Cybernetics10.1109/ICSMC.1994.400002(1166-1171)Online publication date: 1994
https://doi.org/10.1109/ICSMC.1994.400002

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Polynomial uniform convergence and polynomial-sample learnability

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Polynomial uniform convergence and polynomial-sample learnability

Linear partial q-difference equations on q-linear lattices and their bivariate q-orthogonal polynomial solutions

Certain classes of polynomial expansions and multiplication formulas

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