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Computational Learning Theory

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Cover of 'Computational Learning Theory'

Table of Contents

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    Book Overview
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    Chapter 165 The discovery of algorithmic probability: A guide for the programming of true creativity
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    Chapter 166 A desicion-theoretic generalization of on-line learning and an application to boosting
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    Chapter 167 Online learning versus offline learning
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    Chapter 168 Learning distributions by their density levels — A paradigm for learning without a teacher
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    Chapter 169 Tight worst-case loss bounds for predicting with expert advice
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    Chapter 170 On-line maximum likelihood prediction with respect to general loss functions
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    Chapter 171 The power of procrastination in inductive inference: How it depends on used ordinal notations
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    Chapter 172 Learnability of Kolmogorov-easy circuit expressions via queries
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    Chapter 173 Trading monotonicity demands versus mind changes
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    Chapter 174 Learning recursive functions from approximations
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    Chapter 175 On the intrinsic complexity of learning
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    Chapter 176 The structure of intrinsic complexity of learning
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    Chapter 177 Kolmogorov numberings and minimal identification
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    Chapter 178 Stochastic complexity in learning
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    Chapter 179 Function learning from interpolation (extended abstract)
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    Chapter 180 Approximation and learning of convex superpositions
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    Chapter 181 Minimum description length estimators under the optimal coding scheme
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    Chapter 182 MDL learning of unions of simple pattern languages from positive examples
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    Chapter 183 A note on the use of probabilities by mechanical learners
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    Chapter 184 Characterizing rational versus exponential learning curves
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    Chapter 185 Is pocket algorithm optimal?
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    Chapter 186 Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks
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    Chapter 187 A space-bounded learning algorithm for axis-parallel rectangles
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    Chapter 188 Learning decision lists and trees with equivalence-queries
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    Chapter 189 Bounding VC-dimension for neural networks: Progress and prospects
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    Chapter 190 Average case analysis of a learning algorithm for μ -DNF expressions
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    Chapter 191 Learning by extended statistical queries and its relation to PAC learning
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    Chapter 192 Typed pattern languages and their learnability
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    Chapter 193 Learning behaviors of automata from shortest counterexamples
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    Chapter 194 Learning of regular expressions by pattern matching
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    Chapter 195 The query complexity of learning some subclasses of context-free grammars
Attention for Chapter 189: Bounding VC-dimension for neural networks: Progress and prospects
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Chapter title
Bounding VC-dimension for neural networks: Progress and prospects
Chapter number 189
Book title
Computational Learning Theory
Published by
Springer, Berlin, Heidelberg, March 1995
DOI 10.1007/3-540-59119-2_189
Book ISBNs
978-3-54-059119-1, 978-3-54-049195-8
Authors

Marek Karpinski, Angus Macintyre, Karpinski, Marek, Macintyre, Angus

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 2 29%
India 1 14%
Czechia 1 14%
Unknown 3 43%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 43%
Student > Doctoral Student 2 29%
Lecturer 1 14%
Student > Master 1 14%
Researcher 1 14%
Other 0 0%
Readers by discipline Count As %
Computer Science 3 43%
Engineering 2 29%
Economics, Econometrics and Finance 1 14%
Neuroscience 1 14%
Earth and Planetary Sciences 1 14%
Other 0 0%