TY - GEN
T1 - Parallel and distributed systems for constructive neural network learning
AU - Fletcher, J.
AU - Obradovic, Z.
N1 - Funding Information:
*Research sponsored in part by the NSF Industry / Univer-
Funding Information:
sity Cooperative Center for the Design of Analog-Digital ASICs (CDADIC) under grant NSF-CDADIC-90-1 and by Washington State University Research Grant 1OC-3970-9966. t Also affiliated with the Mathematical Institute, Belgrade, Yugoslavia.
Funding Information:
Research sponsored in part by the NSF Industry University Cooperative Center for the Design of Analog-Digital ASICs (CDADIC) under grant NSF-CDADIC-90-1 and by Washington State University Research Grant 1OC-3970-9966.
Publisher Copyright:
© 1993 IEEE.
PY - 1993
Y1 - 1993
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84953478623&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953478623&partnerID=8YFLogxK
U2 - 10.1109/HPDC.1993.263844
DO - 10.1109/HPDC.1993.263844
M3 - Conference contribution
AN - SCOPUS:84953478623
T3 - Proceedings of the IEEE International Symposium on High Performance Distributed Computing
SP - 174
EP - 178
BT - Proceedings of the 2nd International Symposium on High Performance Distributed Computing, HPDC 1993
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Symposium on High Performance Distributed Computing, HPDC 1993
Y2 - 20 July 1993 through 23 July 1993
ER -