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Algorithmic Learning Theory

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Cover of 'Algorithmic Learning Theory'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 String Pattern Discovery
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    Chapter 2 Applications of Regularized Least Squares to Classification Problems
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    Chapter 3 Probabilistic Inductive Logic Programming
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    Chapter 4 Hidden Markov Modelling Techniques for Haplotype Analysis
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    Chapter 5 Learning, Logic, and Probability: A Unified View
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    Chapter 6 Learning Languages from Positive Data and Negative Counterexamples
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    Chapter 7 Inductive Inference of Term Rewriting Systems from Positive Data
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    Chapter 8 On the Data Consumption Benefits of Accepting Increased Uncertainty
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    Chapter 9 Comparison of Query Learning and Gold-Style Learning in Dependence of the Hypothesis Space
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    Chapter 10 Learning r -of- k Functions by Boosting
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    Chapter 11 Boosting Based on Divide and Merge
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    Chapter 12 Learning Boolean Functions in AC 0 on Attribute and Classification Noise
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    Chapter 13 Decision Trees: More Theoretical Justification for Practical Algorithms
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    Chapter 14 Application of Classical Nonparametric Predictors to Learning Conditionally I.I.D. Data
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    Chapter 15 Complexity of Pattern Classes and Lipschitz Property
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    Chapter 16 On Kernels, Margins, and Low-Dimensional Mappings
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    Chapter 17 Estimation of the Data Region Using Extreme-Value Distributions
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    Chapter 18 Maximum Entropy Principle in Non-ordered Setting
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    Chapter 19 Universal Convergence of Semimeasures on Individual Random Sequences
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    Chapter 20 A Criterion for the Existence of Predictive Complexity for Binary Games
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    Chapter 21 Full Information Game with Gains and Losses
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    Chapter 22 Prediction with Expert Advice by Following the Perturbed Leader for General Weights
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    Chapter 23 On the Convergence Speed of MDL Predictions for Bernoulli Sequences
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    Chapter 24 Relative Loss Bounds and Polynomial-Time Predictions for the k-lms-net Algorithm
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    Chapter 25 On the Complexity of Working Set Selection
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    Chapter 26 Convergence of a Generalized Gradient Selection Approach for the Decomposition Method
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    Chapter 27 Newton Diagram and Stochastic Complexity in Mixture of Binomial Distributions
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    Chapter 28 Learnability of Relatively Quantified Generalized Formulas
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    Chapter 29 Learning Languages Generated by Elementary Formal Systems and Its Application to SH Languages
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    Chapter 30 New Revision Algorithms
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    Chapter 31 The Subsumption Lattice and Query Learning
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    Chapter 32 Learning of Ordered Tree Languages with Height-Bounded Variables Using Queries
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    Chapter 33 Learning Tree Languages from Positive Examples and Membership Queries
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    Chapter 34 Learning Content Sequencing in an Educational Environment According to Student Needs
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    Chapter 35 Statistical Learning in Digital Wireless Communications
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    Chapter 36 A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks
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    Chapter 37 Approximate Inference in Probabilistic Models
Overall attention for this book and its chapters
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Title
Algorithmic Learning Theory
Published by
Springer Berlin Heidelberg, September 2004
DOI 10.1007/b100989
ISBNs
978-3-54-023356-5, 978-3-54-030215-5
Editors

Ben-David, Shoham, Case, John, Maruoka, Akira

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 2 2%
United Kingdom 2 2%
Japan 2 2%
Italy 1 1%
Germany 1 1%
Switzerland 1 1%
Unknown 85 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 36%
Student > Master 14 14%
Researcher 13 13%
Student > Bachelor 9 9%
Student > Postgraduate 5 5%
Other 10 10%
Unknown 11 11%
Readers by discipline Count As %
Computer Science 47 48%
Engineering 15 15%
Mathematics 9 9%
Unspecified 5 5%
Psychology 3 3%
Other 7 7%
Unknown 11 11%