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Authors: Luminita State 1 ; Catalina Cocianu 2 ; Panayiotis Vlamos 3 and Viorica Stefanescu 2

Affiliations: 1 University of Pitesti, Romania ; 2 Academy of Economic Studies, Romania ; 3 Ionian University, Greece

ISBN: 978-972-8865-89-4

Keyword(s): Hidden Markov Models, learning by examples, Bayesian classification, training algorithm, neural computation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The research reported in the paper aims the development of a suitable neural architecture for implementing the Bayesian procedure in solving pattern recognition problems. The proposed neural system is based on an inhibitive competition installed among the hidden neurons of the computation layer. The local memories of the hidden neurons are computed adaptively according to an estimation model of the parameters of the Bayesian classifier. Also, the paper reports a series of qualitative attempts in analyzing the behavior of a new learning procedure of the parameters an HMM by modeling different types of stochastic dependencies on the space of states corresponding to the underlying finite automaton. The approach aims the development of some new methods in processing image and speech signals in solving pattern recognition problems. Basically, the attempts are stated in terms of weighting processes and deterministic/non deterministic Bayesian procedures.

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Paper citation in several formats:
State L.; Cocianu C.; Vlamos P.; Stefanescu V. and (2007). A CONNECTIONIST APPROACH IN BAYESIAN CLASSIFICATION.In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-972-8865-89-4, pages 185-190. DOI: 10.5220/0002346401850190

@conference{iceis07,
author={Luminita State and Catalina Cocianu and Panayiotis Vlamos and Viorica Stefanescu},
title={A CONNECTIONIST APPROACH IN BAYESIAN CLASSIFICATION},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2007},
pages={185-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002346401850190},
isbn={978-972-8865-89-4},
}

TY - CONF

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A CONNECTIONIST APPROACH IN BAYESIAN CLASSIFICATION
SN - 978-972-8865-89-4
AU - State, L.
AU - Cocianu, C.
AU - Vlamos, P.
AU - Stefanescu, V.
PY - 2007
SP - 185
EP - 190
DO - 10.5220/0002346401850190

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