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## Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

In the nonlinear systems, the pre-knowledge about the exact functional structure between inputs and outputs is mostly either unavailable or insufficient. In this case, the artificial neural networks (ANNs) are useful tools to estimate this functional structure. However, the traditional ANNs with the sum squared error suffer from the approximation and estimation errors in the high dimensional and excessive nonlinear cases. In this context, Bayesian neural networks (BNNs) provide a natural way to alleviate these issues by means of penalizing the excessive complex models. Thus, this approach allows estimating more reliable and robust models in the regression analysis, time series, pattern recognition problems etc. This paper presents a Bayesian learning approach based on Gaussian approximation which estimates the parameters and hyperparameters in the BNNs efficiently. In the application part, the proposed approach is compared with the traditional ANNs in terms of their estimation and prediction performances over an artificial data set.

Bayesian Neural Networks, Bayesian Learning, Gaussian Approach, Fixed Hyperparameters, Gradient based Algorithms
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Primary Language en Mathematics December Articles Orcid: orcid.org/0000-0003-4354-7383Author: Ozan KoacadagliCountry: Turkey Publication Date : December 29, 2017
 Bibtex @research article { forecasting346891, journal = {Turkish Journal of Forecasting}, issn = {}, eissn = {2618-6594}, address = {Giresun Üniversitesi Fen Edebiyat Fakültesi İstatistik Bölümü, Güre Yerleşkesi, 28100 Merkez, Giresun}, publisher = {Giresun University}, year = {2017}, volume = {01}, pages = {54 - 65}, doi = {}, title = {Bayesian Learning based Gaussian Approximation for Artificial Neural Networks}, key = {cite}, author = {Koacadagli, Ozan} } APA Koacadagli, O . (2017). Bayesian Learning based Gaussian Approximation for Artificial Neural Networks. Turkish Journal of Forecasting , 01 (2) , 54-65 . Retrieved from https://dergipark.org.tr/en/pub/forecasting/issue/33413/346891 MLA Koacadagli, O . "Bayesian Learning based Gaussian Approximation for Artificial Neural Networks". Turkish Journal of Forecasting 01 (2017 ): 54-65 Chicago Koacadagli, O . "Bayesian Learning based Gaussian Approximation for Artificial Neural Networks". Turkish Journal of Forecasting 01 (2017 ): 54-65 RIS TY - JOUR T1 - Bayesian Learning based Gaussian Approximation for Artificial Neural Networks AU - Ozan Koacadagli Y1 - 2017 PY - 2017 N1 - DO - T2 - Turkish Journal of Forecasting JF - Journal JO - JOR SP - 54 EP - 65 VL - 01 IS - 2 SN - -2618-6594 M3 - UR - Y2 - 2017 ER - EndNote %0 Turkish Journal of Forecasting Bayesian Learning based Gaussian Approximation for Artificial Neural Networks %A Ozan Koacadagli %T Bayesian Learning based Gaussian Approximation for Artificial Neural Networks %D 2017 %J Turkish Journal of Forecasting %P -2618-6594 %V 01 %N 2 %R %U ISNAD Koacadagli, Ozan . "Bayesian Learning based Gaussian Approximation for Artificial Neural Networks". Turkish Journal of Forecasting 01 / 2 (December 2017): 54-65 . AMA Koacadagli O . Bayesian Learning based Gaussian Approximation for Artificial Neural Networks. TJF. 2017; 01(2): 54-65. Vancouver Koacadagli O . Bayesian Learning based Gaussian Approximation for Artificial Neural Networks. Turkish Journal of Forecasting. 2017; 01(2): 65-54.