Multi-gradient: A fast converging and high performance learning algorithm

Chulhee Lee, Jinwook Go

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

In this paper, we propose a new learning algorithm for multilayer neural networks. In the backpropagation learning algorithm, weights are adjusted to reduce the error or cost function that reflects the difference between the computed and desired outputs. In the proposed learning algorithm, we consider each term of the output layer as a function of weights and adjust the weights directly so that the output layers produce the desired outputs. Experiments show the proposed algorithm consistently performs better than the back-propagation learning algorithm.

Original languageEnglish
Pages1721-1724
Number of pages4
Publication statusPublished - 1999 Dec 1
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Cite this

Lee, C., & Go, J. (1999). Multi-gradient: A fast converging and high performance learning algorithm. 1721-1724. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .