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Bayesian Analysis for Neural Network Models
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 Title & Authors
Bayesian Analysis for Neural Network Models
Chung, Younshik; Jung, Jinhyouk; Kim, Chansoo;
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 Abstract
Neural networks have been studied as a popular tool for classification and they are very flexible. Also, they are used for many applications of pattern classification and pattern recognition. This paper focuses on Bayesian approach to feed-forward neural networks with single hidden layer of units with logistic activation. In this model, we are interested in deciding the number of nodes of neural network model with p input units, one hidden layer with m hidden nodes and one output unit in Bayesian setup for fixed m. Here, we use the latent variable into the prior of the coefficient regression, and we introduce the `sequential step` which is based on the idea of the data augmentation by Tanner and Wong(1787). The MCMC method(Gibbs sampler and Metropolish algorithm) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data.
 Keywords
Neural network;Latent variab1e;Sequential step;Gibbs sampler;Metropolish algorithm;Transfer function;
 Language
English
 Cited by
1.
Input Variable Importance in Supervised Learning Models,;;

Communications for Statistical Applications and Methods, 2003. vol.10. 1, pp.239-246 crossref(new window)
1.
Input Variable Importance in Supervised Learning Models, Communications for Statistical Applications and Methods, 2003, 10, 1, 239  crossref(new windwow)
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