Probably approximately correct learning

Results: 61



#Item
1Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2  Abstract

Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2 Abstract

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:52:08
2Cross-validation for binary classification by real-valued functions: theoretical analysis Martin Anthony Department of Mathematics London School of Economics Houghton Street

Cross-validation for binary classification by real-valued functions: theoretical analysis Martin Anthony Department of Mathematics London School of Economics Houghton Street

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:24:41
3A Sufficient Condition for Polynomial Distribution-Dependent Learnability Martin Anthony Department of Mathematics London School of Economics Houghton Street

A Sufficient Condition for Polynomial Distribution-Dependent Learnability Martin Anthony Department of Mathematics London School of Economics Houghton Street

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:26:58
4Probabilistic ‘Generalization’ of Functions and Dimension-based Uniform Convergence Results Martin Anthony Department of Mathematics London School of Economics and Political Science

Probabilistic ‘Generalization’ of Functions and Dimension-based Uniform Convergence Results Martin Anthony Department of Mathematics London School of Economics and Political Science

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:58:43
5PAC Learning and Artificial Neural Networks Martin Anthony and Norman Biggs Department of Mathematics, London School of Economics and Political Science (University of London), Houghton St., London WC2A 2AE, United Kingdo

PAC Learning and Artificial Neural Networks Martin Anthony and Norman Biggs Department of Mathematics, London School of Economics and Political Science (University of London), Houghton St., London WC2A 2AE, United Kingdo

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:19:07
6LOWER BOUNDS ON LEARNING RANDOM STRUCTURES WITH STATISTICAL QUERIES 1

LOWER BOUNDS ON LEARNING RANDOM STRUCTURES WITH STATISTICAL QUERIES 1

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Source URL: www.levreyzin.com

Language: English - Date: 2010-10-08 08:44:26
7Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model AVRIM BLUM, ADAM KALAI, AND HAL WASSERMAN Carnegie Mellon University, Pittsburgh, Pennsylvania  Abstract. We describe a slightly subexponential

Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model AVRIM BLUM, ADAM KALAI, AND HAL WASSERMAN Carnegie Mellon University, Pittsburgh, Pennsylvania Abstract. We describe a slightly subexponential

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Source URL: research.microsoft.com

Language: English - Date: 2011-03-02 19:24:24
8Learning Theory  There are various models for Machine Learning. Today will shall describe one such formal model and a few of its variants. This model is called the Probably Approximately Correct (PAC ) learning model. Th

Learning Theory There are various models for Machine Learning. Today will shall describe one such formal model and a few of its variants. This model is called the Probably Approximately Correct (PAC ) learning model. Th

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Source URL: www.eng.tau.ac.il

Language: English
    9Online learning  Adversarial RW Hypercube

    Online learning Adversarial RW Hypercube

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    Source URL: www.cs.technion.ac.il

    Language: English - Date: 2009-11-25 08:34:10
    106.045J Lecture 19: Probably approximately correct (PAC) learning

    6.045J Lecture 19: Probably approximately correct (PAC) learning

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    Source URL: ocw.mit.edu

    Language: English - Date: 2015-05-24 22:10:44