Improving Upon Standard Pattern Classification Algorithms by Implementing them as Multi-Layer Perceptrons.

摘要

The multi-layer perceptron (MLP) is a type of adaptive layered network often used as a pattern classifier. In more recent literature, MLPs are compared with simpler classification techniques using common datasets. We select two of these simple static pattern classification algorithms and briefly review the relevant techniques. After introducing a modest set of evaluation databases, the performance of the standard classifiers and MLPs are assessed. A technique for implementing the two standard classifiers as MLPs is presented and this novel approach is used to automatically design a 'good' set of initial weights for the MLP networks. Encouraging experimental results for these hybrid techniques are shown for illustration. Keywords: Pattern recognition; Speech; Images; Artificial intelligence. (kt)