Automatic feature extraction using a novel noniterative neural network

Chia-Lun John Hu

doi:10.1117/12.359946

24 August 1999 Automatic feature extraction using a novel noniterative neural network

Chia-Lun John Hu

Proceedings Volume 3718, Automatic Target Recognition IX; (1999) https://doi.org/10.1117/12.359946
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

As we reported in the last few years, a one-layered, hard- limited perceptron is generally sufficient for carrying out a robust recognition on any untrained pattern if the training class patterns satisfy a certain PLI condition. For most pattern recognition applications, this condition should be satisfied. When this condition is satisfied, an automatic feature extraction scheme can then be derived using some N- dimension Euclidean geometry theories. This automatic scheme will automatically extract the most distinguished parts of the N-vectors used in the training. These distinguished parts or the feature vectors will then allow a very robust recognition when untrained patterns are tested in the recognition mode. Theoretical derivation and live experiments revealing the physical nature of this novel, ultra-fast learning, pattern recognition system will be presented in detail.

Citation Download Citation

Chia-Lun John Hu "Automatic feature extraction using a novel noniterative neural network", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); https://doi.org/10.1117/12.359946

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