Abstract
| Original language | English |
|---|---|
| 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 |
|---|---|
| Country | New Zealand |
| Period | 22/11/01 → 24/11/01 |
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Building Practical Classifiers Using Cerebellar Model Associative Memory Neural Networks. / Cornforth, David.
Artificial Neural Networks and Expert Systems. ed. / Nikola Kasabov; Brendon Woodford. Dunedin, New Zealand : University of Otago, 2001. p. 1-6.Research output: Book chapter/Published conference paper › Conference paper
TY - GEN
T1 - Building Practical Classifiers Using Cerebellar Model Associative Memory Neural Networks
AU - Cornforth, David
N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Dunedin, New Zealand: University of Otago, 2001. editor/s (773b) = Kasabov, Nikola & Woodford, Brendon; Event dates (773o) = 22-24 November 2001; Parent title (773t) = Conference on Artificial Neural Networks and Expert systems.
PY - 2001
Y1 - 2001
N2 - 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.
AB - 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.
M3 - Conference paper
SP - 1
EP - 6
BT - Artificial Neural Networks and Expert Systems
A2 - Kasabov, Nikola
A2 - Woodford, Brendon
PB - University of Otago
CY - Dunedin, New Zealand
ER -