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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
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    Chapter 1 On Iterative Algorithms with an Information Geometry Background
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    Chapter 2 Visual Analytics: Combining Automated Discovery with Interactive Visualizations
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    Chapter 3 Some Mathematics behind Graph Property Testing
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    Chapter 4 Finding Total and Partial Orders from Data for Seriation
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    Chapter 5 Computational Models of Neural Representations in the Human Brain
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    Chapter 6 Generalization Bounds for Some Ordinal Regression Algorithms
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    Chapter 7 Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm
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    Chapter 8 Sample Selection Bias Correction Theory
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    Chapter 9 Exploiting Cluster-Structure to Predict the Labeling of a Graph
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    Chapter 10 A Uniform Lower Error Bound for Half-Space Learning
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    Chapter 11 Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
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    Chapter 12 Learning and Generalization with the Information Bottleneck
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    Chapter 13 Growth Optimal Investment with Transaction Costs
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    Chapter 14 Online Regret Bounds for Markov Decision Processes with Deterministic Transitions
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    Chapter 15 On-Line Probability, Complexity and Randomness
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    Chapter 16 Prequential Randomness
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    Chapter 17 Some Sufficient Conditions on an Arbitrary Class of Stochastic Processes for the Existence of a Predictor
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    Chapter 18 Nonparametric Independence Tests: Space Partitioning and Kernel Approaches
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    Chapter 19 Supermartingales in Prediction with Expert Advice
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    Chapter 20 Aggregating Algorithm for a Space of Analytic Functions
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    Chapter 21 Smooth Boosting for Margin-Based Ranking
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    Chapter 22 Learning with Continuous Experts Using Drifting Games
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    Chapter 23 Entropy Regularized LPBoost
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    Chapter 24 Optimally Learning Social Networks with Activations and Suppressions
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    Chapter 25 Active Learning in Multi-armed Bandits
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    Chapter 26 Query Learning and Certificates in Lattices
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    Chapter 27 Algorithmic Learning Theory
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    Chapter 28 Active Learning of Group-Structured Environments
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    Chapter 29 Finding the Rare Cube
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    Chapter 30 Iterative Learning of Simple External Contextual Languages
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    Chapter 31 Topological Properties of Concept Spaces
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    Chapter 32 Dynamically Delayed Postdictive Completeness and Consistency in Learning
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    Chapter 33 Dynamic Modeling in Inductive Inference
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    Chapter 34 Optimal Language Learning
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    Chapter 35 Numberings Optimal for Learning
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    Chapter 36 Learning with Temporary Memory
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    Chapter 37 Erratum: Constructing Multiclass Learners from Binary Learners: A Simple Black-Box Analysis of the Generalization Errors
Overall attention for this book and its chapters
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8 Wikipedia pages

Citations

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Title
Algorithmic Learning Theory
Published by
Springer Science & Business Media, September 2008
DOI 10.1007/978-3-540-87987-9
ISBNs
978-3-54-087986-2, 978-3-54-087987-9
Editors

Freund, Yoav, Györfi, László, Turán, György, Zeugmann, Thomas

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 33%
Unknown 2 67%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 33%
Unknown 2 67%