Algorithmic learning theory : 17th international conference ALT 2006 : Barcelona, Spain, October 2006 : proceedings

Table of Contents

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   Book Overview
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   Chapter 1 Editors’ Introduction
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   Chapter 2 Solving Semi-infinite Linear Programs Using Boosting-Like Methods
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   Chapter 3 Algorithmic Learning Theory
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   Chapter 4 Spectral Norm in Learning Theory: Some Selected Topics
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   Chapter 5 Data-Driven Discovery Using Probabilistic Hidden Variable Models
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   Chapter 6 Reinforcement Learning and Apprenticeship Learning for Robotic Control
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   Chapter 7 Learning Unions of ω (1)-Dimensional Rectangles
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   Chapter 8 On Exact Learning Halfspaces with Random Consistent Hypothesis Oracle
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    Chapter 9 Active Learning in the Non-realizable Case
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    Chapter 10 How Many Query Superpositions Are Needed to Learn?
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    Chapter 11 Teaching Memoryless Randomized Learners Without Feedback
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    Chapter 12 The Complexity of Learning SUBSEQ ( A )
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    Chapter 13 Mind Change Complexity of Inferring Unbounded Unions of Pattern Languages from Positive Data
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    Chapter 14 Learning and Extending Sublanguages
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    Chapter 15 Iterative Learning from Positive Data and Negative Counterexamples
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    Chapter 16 Towards a Better Understanding of Incremental Learning
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    Chapter 17 On Exact Learning from Random Walk
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    Chapter 18 Risk-Sensitive Online Learning
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    Chapter 19 Leading Strategies in Competitive On-Line Prediction
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    Chapter 20 Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring
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    Chapter 21 General Discounting Versus Average Reward
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    Chapter 22 The Missing Consistency Theorem for Bayesian Learning: Stochastic Model Selection
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    Chapter 23 Is There an Elegant Universal Theory of Prediction?
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    Chapter 24 Learning Linearly Separable Languages
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    Chapter 25 Smooth Boosting Using an Information-Based Criterion
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    Chapter 26 Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
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    Chapter 27 Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence
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    Chapter 28 Probabilistic Generalization of Simple Grammars and Its Application to Reinforcement Learning
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    Chapter 29 Unsupervised Slow Subspace-Learning from Stationary Processes
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    Chapter 30 Learning-Related Complexity of Linear Ranking Functions