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Neural Computation

Fall 1991, Vol. 3, No. 3, Pages 418-427
(doi: 10.1162/neco.1991.3.3.418)
© 1991 Massachusetts Institute of Technology
Including Hints in Training Neural Nets
Article PDF (464.17 KB)
Abstract

The aim of a neural net is to partition the data space into near optimal decision regions. Learning such a partitioning solely from examples has proven to be a very hard problem (Blum and Rivest 1988; Judd 1988). To remedy this, we use the idea of supplying hints to the network — as discussed by Abu-Mostafa (1990). Hints reduce the solution space, and as a consequence speed up the learning process. The minimum Hamming distance between the patterns serves as the hint. Next, it is shown how to learn such a hint and how to incorporate it into the learning algorithm. Modifications in the net structure and its operation are suggested, which allow for a better generalization. The sensitivity to errors in such a hint is studied through some simulations.