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

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

ISBN: 972-8865-12-0

Keyword(s): Neural Networks, Competitive Learning, Hidden Markov Models, Pattern Recognition, Bayesian Learning, Weighting Processes, Markov Chains.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control ; Signal Reconstruction

Abstract: The research reported in the paper aims the development of a suitable neural architecture for implementing the Bayesian procedure for 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. The aims were mainly to derive asym ptotical conclusions concerning the performance of the proposed estimation techniques in approximating the ideal Bayesian procedure. The proposed methodology adopts the standard assumptions on the conditional independence properties of the involved stochastic processes. (More)

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Paper citation in several formats:
State L.; Cocianu C.; Stefanescu V.; Panayiotis V. and (2004). A NEURAL NETWORK FRAMEWORK FOR IMPLEMENTING THE BAYESIAN LEARNING.In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 972-8865-12-0, pages 328-331. DOI: 10.5220/0001127503280331

@conference{icinco04,
author={Luminita State and Catalina Cocianu and Viorica Stefanescu and Vlamos Panayiotis},
title={A NEURAL NETWORK FRAMEWORK FOR IMPLEMENTING THE BAYESIAN LEARNING},
booktitle={Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2004},
pages={328-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001127503280331},
isbn={972-8865-12-0},
}

TY - CONF

JO - Proceedings of the First International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - A NEURAL NETWORK FRAMEWORK FOR IMPLEMENTING THE BAYESIAN LEARNING
SN - 972-8865-12-0
AU - State, L.
AU - Cocianu, C.
AU - Stefanescu, V.
AU - Panayiotis, V.
PY - 2004
SP - 328
EP - 331
DO - 10.5220/0001127503280331

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