Lorrentz, P and Howells, WGJ and McDonald-Maier, KD (2010) Model learning from weights by adaptive enhanced probabilistic convergent network. In: UNSPECIFIED, ? - ?.

Preview

Text Model_Learning_from_Weights_by_Adaptive_Enhanced_P.pdf - Accepted Version Download (704kB) | Preview

Abstract

Current weightless classifiers require historical data to model a system and make prediction about a system successfully. Historical data either is not always available or does not take a recent system modification into consideration. For this reason an adaptive filter is designed, which when employed with a weightless classifier enables system model, difficult to characterise system model, and system output prediction, successfully. Results of experiments performed show that the fusion of an adaptive filter and a weightless classifier is more beneficial than the filter or the classifier utilised singly, and that no speed advantage is observed.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Published proceedings: Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Divisions:	Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User:	Elements
Date Deposited:	05 Jun 2020 08:16
Last Modified:	05 Jun 2020 08:16
URI:	http://repository.essex.ac.uk/id/eprint/27791

Actions (login required)

View Item