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Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning
(2002-12-01)
In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by ...
Time-series prediction with BEMCA approach: Application to short rainfall series
(Institute of Electrical and Electronics Engineers Inc., 2018)
This paper presents a new method to forecast short rainfall time-series. The new framework is by means of Bayesian enhanced modified combined approach (BEMCA) using permutation and relative entropy with Bayesian inference. ...
Forecasting Electric Load Demand through Advanced Statistical Techniques
(Institute of Physics Publishing, 2020-01-07)
Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate ...
Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging
(Kharkiv National University of Radio Electronics (KhNURE), 2008)
A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm
Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution ...
Bayesian approximations in randomized response model
(Elsevier B.V., 1997-06-05)
Practical Bayesian inference depends upon detailed examination of posterior distribution. When the prior and likelihood are conjugate, this is easily carried out; however, in general, one must resort to numerical approximation. ...