Machine Learning: ECML-98

Table of Contents

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   Book Overview
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   Chapter 1 Learning in agent-oriented worlds
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   Chapter 2 Naive (Bayes) at forty: The independence assumption in information retrieval
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   Chapter 3 Learning verbal transitivity using loglinear models
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   Chapter 4 Part-of-speech tagging using decision trees
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   Chapter 5 Inference of finite automata: Reducing the search space with an ordering of pairs of states
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   Chapter 6 Automatic acquisition of lexical knowledge from sparse and noisy data
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   Chapter 7 A normalization method for contextual data: Experience from a large-scale application
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   Chapter 8 Machine Learning: ECML-98
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    Chapter 9 ILP experiments in detecting traffic problems
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    Chapter 10 Simulating children learning and explaining elementary heat transfer phenomena: A multistrategy system at work
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    Chapter 11 Bayes optimal instance-based learning
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    Chapter 12 Bayesian and information-theoretic priors for Bayesian network parameters
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    Chapter 13 Feature subset selection in text-learning
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    Chapter 14 A monotonic measure for optimal feature selection
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    Chapter 15 Inducing models of human control skills
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    Chapter 16 God doesn't always shave with Occam's razor — Learning when and how to prune
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    Chapter 17 Error estimators for pruning regression trees
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    Chapter 18 Pruning decision trees with misclassification costs
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    Chapter 19 Text categorization with Support Vector Machines: Learning with many relevant features
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    Chapter 20 A short note about the application of polynomial kernels with fractional degree in Support Vector Learning
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    Chapter 21 Classification learning using all rules
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    Chapter 22 Improved pairwise coupling classification with correcting classifiers
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    Chapter 23 Experiments on solving multiclass learning problems by n 2-classifier
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    Chapter 24 Combining classifiers by constructive induction
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    Chapter 25 Boosting trees for cost-sensitive classifications
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    Chapter 26 Naive bayesian classifier committees
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    Chapter 27 Batch classifications with discrete finite mixtures
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    Chapter 28 Induction of recursive program schemes
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    Chapter 29 Predicate invention and learning from positive examples only
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    Chapter 30 An inductive logic programming framework to learn a concept from ambiguous examples
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    Chapter 31 First-order learning for Web mining
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    Chapter 32 Explanation-based generalization in game playing: Quantitative results
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    Chapter 33 Scope classification: An instance-based learning algorithm with a rule-based characterisation
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    Chapter 34 Error-correcting output codes for local learners
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    Chapter 35 Recursive lazy learning for modeling and control
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    Chapter 36 Using lattice-based framework as a tool for feature extraction
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    Chapter 37 Determining property relevance in concept formation by computing correlation between properties
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    Chapter 38 A buffering strategy to avoid ordering effects in clustering
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    Chapter 39 Coevolutionary, distributed search for inducing concept descriptions
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    Chapter 40 Continuous mimetic evolution
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    Chapter 41 A host-parasite genetic algorithm for asymmetric tasks
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    Chapter 42 Speeding up Q(λ)-learning
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    Chapter 43 Q-learning and redundancy reduction in classifier systems with internal state
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    Chapter 44 Composing functions to speed up reinforcement learning in a changing world
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    Chapter 45 Theoretical results on reinforcement learning with temporally abstract options
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    Chapter 46 A general convergence method for Reinforcement Learning in the continuous case
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    Chapter 47 Interpretable neural networks with BP-SOM
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    Chapter 48 Convergence rate of minimization learning for neural networks