A Framework for Grid-based Neural Networks

Authors:
Tan Ker Vin;Michael Pang Zhe Seng;Ng Pek Kuan;Fazilah Haron
Affiliations:
University Sains Malaysia, Penang, Malaysia;University Sains Malaysia, Penang, Malaysia;University Sains Malaysia, Penang, Malaysia;University Sains Malaysia, Penang, Malaysia
Venue:
DFMA '05 Proceedings of the First International Conference on Distributed Frameworks for Multimedia Applications
Year:
2005

Citing 0
Cited 4

Study of neural net training methods in parallel and distributed architectures

Future Generation Computer Systems
Parallel implementation of a neural net training application in a heterogeneous grid environment

OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part II
Parallel batch pattern BP training algorithm of recurrent neural network

INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Parallel Approach for Ensemble Learning with Locally Coupled Neural Networks

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

The usage of neural networks in critical systems is fast approaching the norm. In such systems, the issue of accuracy is of prime importance. Ensemble methods have commonly be used to increase accuracy but at the cost of training time and computational requirements. With the advent of grid computing, it is expected that these problems will be solved. This paper will propose a framework in which an ensemble of neural networks will be implemented over a grid-based environment.