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From J.A. Bullinaria, D.G. Glasspool & G. Houghton (Eds.),
Proceedings of the Fourth Neural Computation and Psychology
Workshop: Connectionist Representations (pp. 58-70). London, U.K.:
Springer-Verlag, 1998.
Improving Learning and
Generalization in Neural Networks through the Acquisition of Multiple
Related Functions
Morten H. Christiansen
Abstract
This paper presents evidence from connectionist simulations
providing support for the idea that forcing neural networks to learn
several related functions together results in both improved learning
and better generalization. More specifically, if a neural network
employing gradient descent learning is forced to capture the
regularities of many semi-correlated sources of information within the
same representational substrate, it then becomes necessary for it to
only represent hypotheses that are consistent with all the cues
provided. When the different sources of information are sufficiently
correlated the number of candidate solutions will be reduced through
the development of more efficient representations. To illustrate
this, the paper draws briefly on research in the neural network
engineering literature, while focusing on recent work on the
segmentation of speech using connectionist networks. Finally, some
implications for language acquisition of the present approach are
discussed.
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