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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Editors’ Introduction
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    Chapter 2 Declarative Modeling for Machine Learning and Data Mining
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    Chapter 3 Learnability beyond Uniform Convergence
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    Chapter 4 Some Rates of Convergence for the Selected Lasso Estimator
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    Chapter 5 Recent Developments in Pattern Mining
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    Chapter 6 Exploring Sequential Data
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    Chapter 7 Enlarging Learnable Classes
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    Chapter 8 Confident and Consistent Partial Learning of Recursive Functions
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    Chapter 9 Automatic Learning from Positive Data and Negative Counterexamples
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    Chapter 10 Regular Inference as Vertex Coloring
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    Chapter 11 Sauer’s Bound for a Notion of Teaching Complexity
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    Chapter 12 On the Learnability of Shuffle Ideals
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    Chapter 13 New Analysis and Algorithm for Learning with Drifting Distributions
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    Chapter 14 On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples
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    Chapter 15 Efficient Protocols for Distributed Classification and Optimization
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    Chapter 16 The Safe Bayesian
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    Chapter 17 Data Stability in Clustering: A Closer Look
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    Chapter 18 Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
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    Chapter 19 Algorithmic Learning Theory
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    Chapter 20 Algorithmic Learning Theory
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    Chapter 21 Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret
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    Chapter 22 Online Prediction under Submodular Constraints
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    Chapter 23 Lower Bounds on Individual Sequence Regret
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    Chapter 24 A Closer Look at Adaptive Regret
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    Chapter 25 Partial Monitoring with Side Information
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    Chapter 26 PAC Bounds for Discounted MDPs
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    Chapter 27 Buy Low, Sell High
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    Chapter 28 Kernelization of Matrix Updates, When and How?
  30. Altmetric Badge
    Chapter 29 Algorithmic Learning Theory
Overall attention for this book and its chapters
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Title
Algorithmic Learning Theory
Published by
Springer Berlin Heidelberg, October 2012
DOI 10.1007/978-3-642-34106-9
ISBNs
978-3-64-234105-2, 978-3-64-234106-9
Editors

Bshouty, Nader H., Stoltz, Gilles, Vayatis, Nicolas, Zeugmann, Thomas

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%

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