MLP modular versus yprel classifiers
Identifieur interne : 003104 ( Main/Exploration ); précédent : 003103; suivant : 003105MLP modular versus yprel classifiers
Auteurs : Yves Lecourtier [France] ; Bernadette Dorizzi [France] ; Philippe Sebire [France] ; Abdel Ennaji [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1993.
Abstract
Abstract: We present two connectionist modular approaches which are potentially able to deal with real applications as their size does not increase drastically with the size of the problem. The first model relics on a very simple cooperation of modular MLP networks specially designed for some sub-tasks. The second is based on a new methodology using a particular processing element (“neuron”) called yprel. The main characteristics of the approach are: (i) An yprel classifier is a set of yprel nets, each net being associated to a particular class; (ii) the learning is supervised and conducted class by class; (iii) the structure of the net is not a priori chosen, but is determined step by step during the learning process. Both approaches are compared on a well-known classification task (recognition of typographic characters) in terms of performance rates.
Url:
DOI: 10.1007/3-540-56798-4_204
Affiliations:
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The first model relics on a very simple cooperation of modular MLP networks specially designed for some sub-tasks. The second is based on a new methodology using a particular processing element (“neuron”) called yprel. The main characteristics of the approach are: (i) An yprel classifier is a set of yprel nets, each net being associated to a particular class; (ii) the learning is supervised and conducted class by class; (iii) the structure of the net is not a priori chosen, but is determined step by step during the learning process. 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