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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
  2. Altmetric Badge
    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
  12. Altmetric Badge
    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 166: A desicion-theoretic generalization of on-line learning and an application to boosting
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Chapter title
A desicion-theoretic generalization of on-line learning and an application to boosting
Chapter number 166
Book title
Computational Learning Theory
Published by
Springer, Berlin, Heidelberg, March 1995
DOI 10.1007/3-540-59119-2_166
Book ISBNs
978-3-54-059119-1, 978-3-54-049195-8

Yoav Freund, Robert E. Schapire

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,903 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 53 3%
Germany 21 1%
United Kingdom 15 <1%
France 13 <1%
Spain 9 <1%
Canada 9 <1%
China 8 <1%
Japan 7 <1%
Czechia 4 <1%
Other 70 4%
Unknown 1694 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 569 30%
Student > Master 390 20%
Researcher 272 14%
Student > Bachelor 151 8%
Professor > Associate Professor 95 5%
Other 282 15%
Unknown 144 8%
Readers by discipline Count As %
Computer Science 952 50%
Engineering 349 18%
Mathematics 86 5%
Agricultural and Biological Sciences 72 4%
Business, Management and Accounting 35 2%
Other 200 11%
Unknown 209 11%