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)