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The role of artificial intelligence in early detection and risk prediction of ischemic heart disease. [PDF]
Ann Med Surg (Lond)Azami P, Kojuri J, Razeghian-Jahromi I.
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LEARNING RULES FOR ASSOCIATIVE MEMORIES
Modern Physics Letters B, 1991We discuss some of the most popular learning rules that can be used to construct Neural Networks that act as associative memories. The Hebb’s rule, perceptron type algorithms and the projector rule with local versions are included.
EDUARDO G. VERGINI, MARCELO G. BLATT
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Learning rules for aversive associative memory formation
Current Opinion in Neurobiology, 2018For survival, organisms need the ability to flexibly modify their behavior. To achieve this, the brain is equipped with instructive brain circuits which trigger changes in neural connectivity and adaptive changes in behavior in response to environmental/internal challenges.
Takaaki, Ozawa, Joshua P, Johansen
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Learning activation rules for associative networks
Proceedings of International Conference on Neural Networks (ICNN'96), 2002In neural networks involving associative recall, information is sometimes encoded using a local representation and predetermined, fired connection weights. Standard learning algorithms that alter weights are not useful in this situation as it is the activation rule that needs to be learned.
J.A. Reggia, E. Grundstrom, R.S. Berndt
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Learning invariants using association rules technique
2012 International Conference on Information Technology and e-Services, 2012Dynamic invariant detection is the identification of properties of programs by analyzing execution traces. Traditional dynamic invariant detectors, such as Daikon, use naive techniques based on verification of predefined invariant forms. Unfortunately, this may discard many useful knowledge such as relationship between variables. This kind of knowledge
M. A. Souaiaia, T. Benouhiba
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Learning Business Rules with Association Rule Classifiers
2014The main obstacles for a straightforward use of association rules as candidate business rules are the excessive number of rules discovered even on small datasets, and the fact that contradicting rules are generated. This paper shows that Association Rule Classification algorithms, such as CBA, solve both these problems, and provides a practical guide ...
Tomáš Kliegr +3 more
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