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<html><head><title>Netlab Reference Manual demev3</title></head><body><H1> demev3</H1><h2>Purpose</h2>Demonstrate Bayesian regression for the RBF.<p><h2>Synopsis</h2><PRE>demev3</PRE><p><h2>Description</h2>The problem consists an input variable <CODE>x</CODE> which sampled from aGaussian distribution, and a target variable <CODE>t</CODE> generated bycomputing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. An RBFnetwork with linear outputs is trained by minimizing a sum-of-squareserror function with isotropic Gaussian regularizer, using the scaledconjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph is plotted of the original function, the training data, the trainednetwork function, and the error bars.<p><h2>See Also</h2><CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="netevfwd.htm">netevfwd</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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