Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Bayesian approach for neural networks—review and case studies
Neural Networks
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Support vector regression based shear strength modelling of deep beams
Computers and Structures
Expert Systems with Applications: An International Journal
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This paper gives a concise overview of three approaches to nonlinear regression modelling with feed-forward neural networks, involving the use of evidence framework and full Bayesian inference with Markov chain Monte Carlo stochastic sampling. The article then presents an empirical assessment of these approaches using a benchmark regression problem for compressive strength prediction of high-performance concrete. Results on applying various methods to benchmark dataset show that Bayesian approach with the MCMC sampling approximation of learning and prediction gives the best prediction accuracy.