A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers

Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)

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Kenney Ng, Richard P. Lippmann


Seven different pattern classifiers were implemented on a serial computer and compared using artificial and speech recognition tasks. Two neural network (radial basis function and high order polynomial GMDH network) and five conventional classifiers (Gaussian mixture, linear tree, K nearest neighbor, KD-tree, and condensed K nearest neighbor) were evaluated. Classifiers were chosen to be representative of different approaches to pat(cid:173) tern classification and to complement and extend those evaluated in a previous study (Lee and Lippmann, 1989). This and the previous study both demonstrate that classification error rates can be equivalent across different classifiers when they are powerful enough to form minimum er(cid:173) ror decision regions, when they are properly tuned, and when sufficient training data is available. Practical characteristics such as training time, classification time, and memory requirements, however, can differ by or(cid:173) ders of magnitude. These results suggest that the selection of a classifier for a particular task should be guided not so much by small differences in error rate, but by practical considerations concerning memory usage, com(cid:173) putational resources, ease of implementation, and restrictions on training and classification times.