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Parallel and distributed systems for constructive neural network learning

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2 Author(s)
J. Fletcher ; Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA ; Z. Obradovic

A constructive learning algorithm dynamically creates a problem-specific neural network architecture rather than learning on a pre-specified architecture. The authors propose a parallel version of their recently presented constructive neural network learning algorithm. Parallelization provides a computational speedup by a factor of O(t) where t is the number of training examples. Distributed and parallel implementations under p4 using a network of workstations and a Touchstone DELTA are examined. Experimental results indicate that algorithm parallelization may result not only in improved computational time, but also in better prediction quality

Published in:

High Performance Distributed Computing, 1993., Proceedings the 2nd International Symposium on

Date of Conference:

20-23 Jul 1993