2 March 1994 Efficient autonomous learning for statistical pattern recognition
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Abstract
We describe a neural network learning algorithm that implements differential learning in a generalized backpropagation framework. The algorithm regulates model complexity during the learning procedure, generating the best low-complexity approximation for the Bayes-optimal classifier allowed by the training sample. It learns to recognize handwritten digits of the AT&T DB1 database. Learning is done with little human intervention. The algorithm generates a simple neural network classifier from the benchmark partitioning of the database; the classifier has 650 total parameters and exhibits a test sample error rate of 1.3%.
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John B. Hampshire, John B. Hampshire, Bhagavatula Vijaya Kumar, Bhagavatula Vijaya Kumar, } "Efficient autonomous learning for statistical pattern recognition", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169956; https://doi.org/10.1117/12.169956
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