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<html><head><title>Netlab Reference Manual demev2</title></head><body><H1> demev2</H1><h2>Purpose</h2>Demonstrate Bayesian classification for the MLP.<p><h2>Synopsis</h2><PRE>demev2</PRE><p><h2>Description</h2>A synthetic two class two-dimensional dataset <CODE>x</CODE> is sampled from a mixture of four Gaussians. Each class isassociated with two of the Gaussians so that the optimal decisionboundary is non-linear.A 2-layernetwork with logistic outputs is trained by minimizing the cross-entropyerror function with isotroipc Gaussian regularizer (one hyperparameter foreach of the four standard weight groups), using the scaledconjugate gradient optimizer. The hyperparameter vectors <CODE>alpha</CODE> and<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph is plotted of the optimal, regularised, and unregularised decisionboundaries. A further plot of the moderated versus unmoderated contoursis generated.<p><h2>See Also</h2><CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE>, <CODE><a href="demmlp2.htm">demmlp2</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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