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Proceedings Paper

Efficient autonomous learning for statistical pattern recognition
Author(s): John B. Hampshire; Bhagavatula Vijaya Kumar
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Paper 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%.

Paper Details

Date Published: 2 March 1994
PDF: 21 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169956
Show Author Affiliations
John B. Hampshire, Jet Propulsion Lab. (United States)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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