Yıl 2017, Cilt 01, Sayı 2, Sayfalar 54 - 65 2017-12-29

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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Konular Matematik ve İstatistik
Yayımlanma Tarihi Aralık 2017
Dergi Bölümü Articles

Orcid: orcid.org/0000-0003-4354-7383
Yazar: Ozan Koacadagli
E-posta: ozan.kocadagli@msgsu.edu.tr
Ülke: Turkey

Bibtex @araştırma makalesi { forecasting346891, journal = {Turkish Journal of Forecasting}, issn = {}, address = {Giresun University Forecast Research Laboratory}, 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 http://dergipark.gov.tr/forecasting/issue/33413/346891
MLA Koacadagli, O . "Bayesian Learning based Gaussian Approximation for Artificial Neural Networks". Turkish Journal of Forecasting 01 (2017): 54-65 <http://dergipark.gov.tr/forecasting/issue/33413/346891>
Chicago Koacadagli, O . "Bayesian Learning based Gaussian Approximation for Artificial Neural Networks". Turkish Journal of Forecasting 01 (2017): 54-65
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