13_Ann - Restaurant Data Set Artificial Neural Networks...

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10/21/2009 1 Artificial Neural Networks Restaurant Data Set Limited Expressiveness of Perceptrons Minsky and Papert (1969) showed certain simple functions cannot be represented (e.g. Boolean XOR). Killed the field! Mid 80 th : Non linear Neural Networks (Rumelhart et al. 1986) Neural Networks Rich history, starting in the early forties (McCulloch and Pitts 1943). Two views: Modeling the brain “Just” representation of complex functions (Continuous; contrast decision trees) Much progress on both fronts. Drawn interest from: Neuroscience, Cognitive science, AI, Physics, Statistics, and CS/EE. Neuron Why Neural Nets? Motivation: Solving problems under the constraints similar to those of the brain may lead to solutions to AI problems that would otherwise be overlooked. Individual neurons operate very slowly massively parallel algorithms Neurons are failure prone devices distributed representations Neurons promote approximate matching less brittle
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10/21/2009 2 Connectionist Models of Learning Characterized by: A large number of very simple neuron like processing elements.
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13_Ann - Restaurant Data Set Artificial Neural Networks...

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