Hybrid Data Mining,
Knowledge Discovery and Machine Learning
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Machine
Learning Research: Four Current Directions, T. Dietterich
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Bibliography
on Integration of Symbolism with Connectionism, and Rule Integration and
Extraction in Neural Networks
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Rule
extraction from Neural Networks, R. Andrews, J. Diederich, & L. Giles
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Rule Extraction
From Neural Networks1
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Knowledge-Based
Artificial Neural Networks (KBANN) and its version
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Knowledge-based
ANN for creating advice-taking reinforcement learners.
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Dynamically
Adding Symbolically Meaningful Nodes, W.Opitz, J. Shavlik
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Understanding
Time-Series Networks: A Case Study in Rule Extraction.M. W. Craven &
J. W. Shavlik (1997).
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Applications
of machine learning and rule induction. Langley, P., & Simon, H. A.
(1995), Communications of the ACM, 38, November, 55-64.
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Current
State of Data Mining Software Applications. Data Mining for Time Series.
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Comparison
of NN, kNN, Rule Induction and Decision Trees. Summary
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Composite models
combining rule-based, instance-based and nearest-neighbor models
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Rules switching linear
predictors for numeric forecast Rule
examples Tutorial
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Committees of Decision
Trees (postscript) absract
(html)
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Combining
Symbolic and Neural Learning to Revise Probabilistic Theories
abstract
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Bayesian
Learning for Neural Networks
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Expert
Systems and Probabilistic Network Models
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