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Probably approximately correct learning
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#	Item
1	Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2 Abstract Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:52:08 Machine learning Learning Artificial intelligence Computational learning theory Computational statistics Statistical classification Computational neuroscience VC dimension Artificial neural network Probably approximately correct learning Probability distribution Uniform convergence
2	Cross-validation for binary classification by real-valued functions: theoretical analysis Martin Anthony Department of Mathematics London School of Economics Houghton Street Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:24:41 Machine learning Learning Statistics Cross-validation Probably approximately correct learning Degrees of freedom Cognition Stability Generalization error
3	A Sufficient Condition for Polynomial Distribution-Dependent Learnability Martin Anthony Department of Mathematics London School of Economics Houghton Street Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:26:58 Computational learning theory Machine learning Learning Theoretical computer science Cognition Probably approximately correct learning Sample complexity VC dimension Mode Occam learning Error Tolerance
4	Probabilistic ‘Generalization’ of Functions and Dimension-based Uniform Convergence Results Martin Anthony Department of Mathematics London School of Economics and Political Science Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:58:43 Computational learning theory Machine learning Probably approximately correct learning Uniform convergence VC dimension Probability distribution Stability Generalization error
5	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 Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:19:07 Computational learning theory Artificial neural networks Machine learning Computational statistics Learning VC dimension Sample complexity Probably approximately correct learning Backpropagation Perceptron Vladimir Vapnik Radial basis function network
6	LOWER BOUNDS ON LEARNING RANDOM STRUCTURES WITH STATISTICAL QUERIES 1 Add to Reading List Source URL: www.levreyzin.com Language: English - Date: 2010-10-08 08:44:26 Computational learning theory Machine learning Theoretical computer science Probably approximately correct learning Error Tolerance
7	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 Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2011-03-02 19:24:24 Computational learning theory Machine learning Artificial intelligence Learning Probability distribution Theoretical computer science Probably approximately correct learning Statistical classification Error Tolerance Supervised learning
8	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 Add to Reading List Source URL: www.eng.tau.ac.il Language: English
9	Online learning Adversarial RW Hypercube Add to Reading List Source URL: www.cs.technion.ac.il Language: English - Date: 2009-11-25 08:34:10 Probably approximately correct learning Online machine learning E-learning Learning Education Distance education Computational learning theory
10	6.045J Lecture 19: Probably approximately correct (PAC) learning Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-05-24 22:10:44

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