2 March 1994 Efficient autonomous learning for statistical pattern recognition
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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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