# demev2

## Purpose

Demonstrate Bayesian classification for the MLP.

## Synopsis

    demev2

## Description

A synthetic two class two-dimensional dataset `x` is sampled from a mixture of four Gaussians. Each class is associated with two of the Gaussians so that the optimal decision boundary is non-linear. A 2-layer network with logistic outputs is trained by minimizing the cross-entropy error function with isotroipc Gaussian regularizer (one hyperparameter for each of the four standard weight groups), using the scaled conjugate gradient optimizer. The hyperparameter vectors `alpha` and `beta` are re-estimated using the function `evidence`. A graph is plotted of the optimal, regularised, and unregularised decision boundaries. A further plot of the moderated versus unmoderated contours is generated.

## See Also

`evidence`, `mlp`, `scg`, `demard`, `demmlp2`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)