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Connectionist Techniques


An Introduction to Natural Computation.
Dana H. Ballard.
Bradford, MIT Press. Cambridge, London. 1999.

From Word Stream to Gestalt: A Direct Semantic Parse for Complex Sentences. Technical Report AI 98-274.
Bobby



http://www.lt-world.org/hlt_survey/ltw-chapter11-5.pdf


Connectionist techniques are modelled on biological brains, whose higher-order cognitive processes appear to emerge from the interplay of large numbers of simple processing units, the neurons. Rather than being used as a substrate in which to implement known elements playing known roles, neural networks are let to evolve by themselves: they gradually adapt to the environment through a modification of inter-neural connection strengths, which come to reflect the neurons' history of co-activities. Typically, the emerging network represents objects, symbols, attributes, etc. (if at all) in states, involving larger numbers of neurons. Connectionism is a field of machine learning and has an affinity to statistics, fuzzy logic, and genetic programming.


PDP

Parallel Distributed Processing; Connectionism