A new perspective in learning pattern generation for teaching neural networks

G -P.K. Economou; D Lymberopoulos; K Spiropoulos; P D. Goumas

doi:10.1016/s0893-6080(99)00021-0

A new perspective in learning pattern generation for teaching neural networks

Neural Netw. 1999 Jun;12(4-5):767-775. doi: 10.1016/s0893-6080(99)00021-0.

Authors

G -P.K. Economou¹, D Lymberopoulos, K Spiropoulos, P D. Goumas

Affiliation

¹ Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

PMID: 12662682
DOI: 10.1016/s0893-6080(99)00021-0

Abstract

This article deals with the Learning Patterns (LPs)' generation, a major aspect of Feed-Forward Artificial Neural Networks (FANNs)' learning process. Currently, more work is done to understand the mechanisms and improve the speed, learning accuracy, and implementation features of FANNs' teaching algorithms, though little is done towards the development of enhanced techniques that would extract experts' knowledge (from examples, rules, etc.) and obtain standardised LPs that would improve this learning process. A new approach in generating LPs is thereby introduced, that is used to train a new Medical Decision Support System (MDSS) based on FANNs, and its performance is analysed and compared with previous methods. It can handle incomplete data archives, individually boost any particular dataum special characteristics, and its application induces the FANNs to show better convergent facets. The efficiency of the resulting MDSS was thoroughly tested by pulmonologists and haematologists using medical data archives of a regional hospital.