Improving learning rate of Neural Tree Networks using thermal perceptrons

Ananth Sankar, Richard J. Mammone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently we proposed a new neural network called Neural Tree Networks (NTN). The NTN is a combination of decision trees and multilayer perceptrons (MLP). The NTN grows the network as opposed to MLPs. The learning algorithm for growing NTNs is more efficient than standard decision tree algorithms. Simulation results have shown that the NTN is superior in performance to both decision trees and MLPs. In this paper a new NTN learning algorithm is proposed based on the thermal perception algorithm. It is shown that the new algorithm greatly increases the speed of learning of the NTN and attains similar classification performance as the previously used algorithm.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing
PublisherPubl by IEEE
Pages90-100
Number of pages11
ISBN (Print)0780301188
StatePublished - 1991
EventProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91 - Princeton, NJ, USA
Duration: Sep 30 1991Oct 2 1991

Publication series

NameNeural Networks for Signal Processing

Other

OtherProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91
CityPrinceton, NJ, USA
Period9/30/9110/2/91

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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