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BEXA: A Covering Algorithm for Learning Propositional Concept Descriptions

https://doi.org/10.1007/bf00117830
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Machine Learning
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Learning from Observation: Conceptual Clustering
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Induction of Decision Trees
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The CN2 Induction Algorithm
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An Empirical Comparison of Selection Measures for Decision-Tree Induction
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Overfitting Avoidance as Bias
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Learning two-tiered descriptions of flexible concepts: The POSEIDON system
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A Further Comparison of Splitting Rules for Decision-Tree Induction
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On the Handling of Continuous-Valued Attributes in Decision Tree Generation
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Rule induction with CN2: Some recent improvements
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Generalization as search
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A theory and methodology of inductive learning
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Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
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variable-valued logic and its applications to pattern recognition and machine learning
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Experiments on the Costs and Benefits of Windowing in ID3
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An SE-tree based Characterization of the Induction Problem
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PRISM: An algorithm for inducing modular rules
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Simplifying decision trees
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Boolean Feature Discovery in Empirical Learning
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Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System
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Capturing knowledge through top-down induction of decision trees
inferredcites →https://doi.org/10.1109/tai.1993.633948openalex-snapshot
An empirical evaluation of beam search and pruning in BEXA
inferredcites →https://doi.org/10.13021/mars/3469openalex-snapshot
INDUCE 2: A Program for Learning Structural Descriptions from Examples
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Classification and Regression Trees.
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Classification and Regression Trees.

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Multi-Task Linear Dependency Modeling for drug-related webpages classification
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