Computational Learning Theory: EuroCOLT '93

Overview


The study of machine learning within the mathematical framework of complexity theory has seen great strides in just a few short years, spurred on by the tremendous rise in interest from engineers studying control to analysts predicting financial market activity. Based on the first European Conference on Computational Learning Theory, and including a number of invited contributions, Computational Learning Theory offers an outstanding overview of the subject, with topics ranging from results inspired by neural ...
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Overview


The study of machine learning within the mathematical framework of complexity theory has seen great strides in just a few short years, spurred on by the tremendous rise in interest from engineers studying control to analysts predicting financial market activity. Based on the first European Conference on Computational Learning Theory, and including a number of invited contributions, Computational Learning Theory offers an outstanding overview of the subject, with topics ranging from results inspired by neural network research to those originating from more classical artificial intelligence approaches. It will appeal to students and researchers in applied mathematics, computer science, and cognitive science.
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Product Details

Meet the Author

University of London

London School of Economics and Political Science

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Table of Contents

On the complexity of learning on neural nets 1
Some new directions in computational learning theory 19
A neuroidal model for cognitive functions 33
Learning rules with local exceptions 35
On learning simple deterministic and probabilistic neural concepts 47
Learning unions of convex polygons 61
On training simple neural networks and small-weight neurons 69
Bounds on the number of examples needed for learning functions 83
Valid generalisation of functions from close approximations on a sample 95
Using experts for predicting continuous outcomes 109
Read-twice DNF formulas are properly learnable 121
Trial and error: a new approach to space-bounded learning 133
Using Kullback-Leibler divergence in learning theory 145
Learning local and recognizable [omega]-languages and monadic logic programs 157
Classification of predicates and languages 171
The neural network loading problem is undecidable 183
On the power of equivalence queries 193
On-line prediction and conversion strategies 205
Learning non-parametric smooth rules by stochastic rules with finite partitioning 217
Improved sample size bounds for PAB-decisions 229
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