Abstract
How can cerebellar model neural networks be successfully applied to automated classification? Simple modifications and a new training scheme are applied to the Cerebellar Model Articulation Controller (CMAC). This results in a classifier with fast training time, guaranteed convergence and a small number of parameters. How can model parameters be set to achieve optimum classifier accuracy, without lengthy empirical trials? A consideration of the most significant sources of classification error results in a simple method for estimating the optimum range for the parameters. The method is tested using empirical trials, and shown to be reliable. This makes the modified CMAC a desirable choice for automated classification in the context of pattern recognition and data mining.
Original language | English |
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Title of host publication | Artificial Neural Networks and Expert Systems |
Editors | Nikola Kasabov, Brendon Woodford |
Place of Publication | Dunedin, New Zealand |
Publisher | University of Otago |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 1877139408 |
Publication status | Published - 2001 |
Event | Conference on Artificial Neural Networks and Expert systems - Dunedin, New Zealand, New Zealand Duration: 22 Nov 2001 → 24 Nov 2001 |
Conference
Conference | Conference on Artificial Neural Networks and Expert systems |
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Country/Territory | New Zealand |
Period | 22/11/01 → 24/11/01 |