Year

1996

Degree Name

Master of Computer Science

Department

Department of Computer Science

Abstract

This thesis introduces a dynamic recognition neural network model (DRNNM) that provides a theoretical basis for the resolution of a number of pattern recognition problems. These problems consist of: The Binding Problem; The Correspondence Problem; The Learning Complexity Problem and The Knowledge Transference and Extension Problem. The Thesis also addresses related issues, such as: recognition with scarce training resources; dynamic feature extraction and a methodology for reducing learning conflict or crosstalk.

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