AUTOMATIC FEATURE EXTRACTION AND FEATURE COMPETITION IN A PERCEPTRON PATTERN RECOGNIZER

摘要

When the dimension N of the input vector is much larger than the number M of different training patterns to be learned, a one-layered, hard-limited perceptron with N input nodes and P neurons (p>=Log{sub}2.M) is generally sufficient to accomplish the learning-recognition task. The recognition should be very robust and very fast if an optimum noniterative learning scheme is applied to the perceptron learning process. This paper concentrates at the discussion of two special characteristics of this novel pattern recognition system: the automatic feature extraction and the automatic feature competition. An unedited video movie recorded on a series of learning-recognition experiments may demonstrate these characteristics of the novel system in real-time.