1994
AUTHORSBhaskar DasGupta , Hava T. Siegelmann , Eduardo Sontag
ABSTRACTNeural networks have been proposed as a tool for machine learning. In this role, a network is trained to recognize complex associations between inputs and outputs that were presented during a supervised training cycle. These associations are incorporated into the weights of the network, which encode a distributed representation of the information that was contained in the patterns. Once trained, the network will compute an input/output mapping which, if the training data was representative enough, will closely match the unknown rule which produced the original data. Massive parallelism of computation, as well as noise and fault tolerance, are often offered as justifications for the use of neural nets as learning paradigms. More... »
PAGES357-389
Theoretical Advances in Neural Computation and Learning
ISBN
978-1-4613-6160-2
978-1-4615-2696-4
http://scigraph.springernature.com/pub.10.1007/978-1-4615-2696-4_10
DOIhttp://dx.doi.org/10.1007/978-1-4615-2696-4_10
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