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7
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Constructing New Attributes for Decision Tree Learning
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Extracting Comprehensible Models from Trained Neural Networks
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4
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Scalability Of Machine Learning Algorithms
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An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning
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122
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14
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Induction over the unexplained: Using overly-general domain theories to aid concept learning
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80
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Induction of Oblique Decision Trees
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176
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The Extraction of Refined Rules from Knowledge-Based Neural Networks
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5
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Hybrid Decision Tree
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9
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Symbol Processing Systems, Connectionist Networks, and Generalized Connectionist Networks
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1
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Paradigms for Machine Learning
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48
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32
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9
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Generativity and Systematicity in Neural Network Combinatorial Learning
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26
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Generative Learning Structures and Processes for Generalized Connectionist Networks
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18
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An Anytime Approach To Connectionist Theory Refinement: Refining The Topologies Of Knowledge-Based Neural Networks
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4
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On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations
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12
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Some Experiments with a Hybrid Model for Learning Sequential Decision Making
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