Feedforward Neural Networks for Nonparametric Regression
نویسنده
چکیده
Feed forward neural networks (FFNN) with an unconstrained random number of hidden neurons deene exible non-parametric regression models. In M uller and Rios Insua (1998) we have argued that variable architecture models with random size hidden layer signiicantly reduce posterior mul-timodality typical for posterior distributions in neural network models. In this chapter we review the model proposed in M uller and Rios Insua (1998) and extend it to a non-parametric model by allowing unconstrained size of the hidden layer. This is made possible by introducing a Markov chain Monte Carlo posterior simulation scheme using reversible jump (Green 1995) steps to move between diierent size architectures.
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