Machine Learning: ECML-93

Table of Contents

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   Book Overview
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   Chapter 124 FOIL: A midterm report
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   Chapter 125 Inductive logic programming: Derivations, successes and shortcomings
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   Chapter 126 Two methods for improving inductive logic programming systems
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   Chapter 127 Generalization under implication by using or-introduction
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   Chapter 128 On the proper definition of minimality in specialization and theory revision
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   Chapter 129 Predicate invention in inductive data engineering
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   Chapter 130 Subsumption and refinement in model inference
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   Chapter 131 Some lower bounds for the computational complexity of inductive logic programming
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    Chapter 132 Improving example-guided unfolding
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    Chapter 133 Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability
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    Chapter 134 Induction of recursive Bayesian classifiers
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    Chapter 135 Decision tree pruning as a search in the state space
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    Chapter 136 Controlled redundancy in incremental rule learning
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    Chapter 137 Getting order independence in incremental learning
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    Chapter 138 Feature selection using rough sets theory
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    Chapter 139 Effective learning in dynamic environments by explicit context tracking
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    Chapter 140 COBBIT—A control procedure for COBWEB in the presence of concept drift
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    Chapter 141 Genetic algorithms for protein tertiary structure prediction
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    Chapter 142 SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts
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    Chapter 143 SAMIA: A bottom-up learning method using a simulated annealing algorithm
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    Chapter 144 Predicate invention in ILP — an overview
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    Chapter 145 Functional inductive logic programming with queries to the user
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    Chapter 146 A note on refinement operators
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    Chapter 147 An iterative and bottom-up procedure for proving-by-example
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    Chapter 148 Learnability of constrained logic programs
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    Chapter 149 Complexity dimensions and learnability
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    Chapter 150 Can complexity theory benefit from Learning Theory?
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    Chapter 151 Learning domain theories using abstract background knowledge
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    Chapter 152 Discovering patterns in EEG-signals: Comparative study of a few methods
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    Chapter 153 Learning to control dynamic systems with automatic quantization
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    Chapter 154 Refinement of rule sets with JoJo
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    Chapter 155 Rule combination in inductive learning
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    Chapter 156 Using heuristics to speed up induction on continuous-valued attributes
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    Chapter 157 Integrating models of knowledge and Machine Learning
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    Chapter 158 Exploiting context when learning to classify
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    Chapter 159 IDDD: An inductive, domain dependent decision algorithm
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    Chapter 160 An application of machine learning in the domain of loan analysis
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    Chapter 161 Extraction of knowledge from data using constrained neural networks
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    Chapter 162 Integrated learning architectures
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    Chapter 163 An overview of evolutionary computation
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    Chapter 164 ML techniques and text analysis