1 April 1997 Automatic feature extraction in neural network noniterative learning
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Abstract
It is proved analytically, whenever the input-output mapping of a one-layered, hard-limited perceptron satisfies a positive, linear independency (PLI) condition, the connection matrix A to meet this mapping can be obtained noniteratively in one step from an algebraic matrix equation containing an N multiplied by M input matrix U. Each column of U is a given standard pattern vector, and there are M standard patterns to be classified. It is also analytically proved that sorting out all nonsingular sub-matrices Uk in U can be used as an automatic feature extraction process in this noniterative-learning system. This paper reports the theoretical derivation and the design and experiments of a superfast-learning, optimally robust, neural network pattern recognition system utilizing this novel feature extraction process. An unedited video movie showing the speed of learning and the robustness in recognition of this novel pattern recognition system is demonstrated in life. Comparison to other neural network pattern recognition systems is discussed.
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Chia-Lun John Hu, Chia-Lun John Hu, } "Automatic feature extraction in neural network noniterative learning", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269775; https://doi.org/10.1117/12.269775
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