Differential learning leads to efficient neural network classifiers
J.B. Hampshire, B. V. K. Vijaya Kumar · IEEE International Conference on Acoustics Speech and Signal Processing · 1993
The authors outline a differential theory of learning for statistical pattern classification. The theory is based on classification figure-of-merit (CFM) objective functions, described by J. P. Hampshire II and A. H. Waibel (IEEE Trans. Neural Netw. vol.1, no.2, p.216-218, June 1990). They give the proof that differential learning is efficient, requiring the least classifier complexity and the smallest training sample size necessary to achieve Bayesian (i.e., minimum error) discrimination. A practical application of the theory is included in which a simple differentially trained linear neural network classifier discriminations handwritten digits of the AT&T DB1 database with a 1.3% error rate. This error rate is less than one half of the best previous result for a linear classifier on this optical character recognition (OCR) task.>