# A NEURAL NETWORK FRAMEWORK FOR IMPLEMENTING THE BAYESIAN LEARNING

### Luminita State, Catalina Cocianu, Viorica Stefanescu, Vlamos Panayiotis

#### 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 asymptotical 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.

#### References

- Bishop, C., 1996, Neural Networks for Pattern Recognition; Oxford University Press
- Devroye, L., Gyorfi, L., Lugosi, G., 1996.; A Probabilistic Theory of Pattern Recognition, Springer Verlag
- Fukunaga, K., 1990, Introduction to Statistical Pattern Recognition, Academic Press
- Lampinen, J., Vehtari, A, 2001 Bayesian Approach for Neural Networks: Review and Case Studies In Neural Networks, Vol. 14
- State, L., Cocianu, C., 2001, Information Based Algorithms in Signal Processing In Proceedings of SYNASC'2001 (The 3rd International Workshop on Symbolic and Numeric Algorithms for Scientific Computation), Timisoara, 3-5 octombrie 2001.
- State, L., Cocianu, C., Vlamos, P., 2002, Nonparametric Approach to Learning the Bayesian Procedure for Hidden Markov Models In Proceedings of SCI2002, Orlando, USA, July 14-18, 2002
- Stewart, W., 1994, Introduction to the Numerical Solutions of Markov Chains; Princeton University Press

#### Paper Citation

#### in Harvard Style

State L., Cocianu C., Stefanescu V. and Panayiotis V. (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

#### in Bibtex Style

@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},

}

#### in EndNote Style

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