Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

Summer 1990, Vol. 2, No. 2, Pages 188-197
(doi: 10.1162/neco.1990.2.2.188)
© 1990 Massachusetts Institute of Technology
Generalizing Smoothness Constraints from Discrete Samples
Article PDF (511.59 KB)
Abstract

We study how certain smoothness constraints, for example, piecewise continuity, can be generalized from a discrete set of analog-valued data, by modifying the error backpropagation, learning algorithm. Numerical simulations demonstrate that by imposing two heuristic objectives — (1) reducing the number of hidden units, and (2) minimizing the magnitudes of the weights in the network — during the learning process, one obtains a network with a response function that smoothly interpolates between the training data.