Algorithmic Learning Theory

Table of Contents

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   Book Overview
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   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