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.
|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|
|Number of pages||6|
|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 on Artificial Neural Networks and Expert systems|
|Period||22/11/01 → 24/11/01|
Cornforth, D. (2001). Building Practical Classifiers Using Cerebellar Model Associative Memory Neural Networks. In N. Kasabov, & B. Woodford (Eds.), Artificial Neural Networks and Expert Systems (pp. 1-6). University of Otago.