Automatic feature extraction and feature competition in a perceptron pattern recognizer

Chia-Lun John Hu

doi:10.1117/12.169975

2 March 1994 Automatic feature extraction and feature competition in a perceptron pattern recognizer

Chia-Lun John Hu

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169975
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

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 > equals Log₂M) 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.

Citation Download Citation

Chia-Lun John Hu "Automatic feature extraction and feature competition in a perceptron pattern recognizer", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169975

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