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back-propagation
(redirected from backpropagation)

   Also found in: Dictionary/thesaurus, Wikipedia 0.06 sec.
back-propagation - (Or "backpropagation") A learning algorithm for modifying a feed-forward neural network which minimises a continuous "error function" or "objective function." Back-propagation is a "gradient descent" method of training in that it uses gradient information to modify the network weights to decrease the value of the error function on subsequent tests of the inputs. Other gradient-based methods from numerical analysis can be used to train networks more efficiently.

Back-propagation makes use of a mathematical trick when the network is simulated on a digital computer, yielding in just two traversals of the network (once forward, and once back) both the difference between the desired and actual output, and the derivatives of this difference with respect to the connection weights.

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In this paper, we compared the prediction of occupational attainment using a backpropagation neural network model and a multinomial logit model Both techniques use variables related to education, experience minority status, disability status, marital status, sex, and geographic region as inputs to perform the prediction.
The NNM facilitates design of artificial neural networks by making the following selection of networks available to users: Feed-forward, Radial Basis Function, Recurrent, Backpropagation and Competitive Learning.
 
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