📄 rbfhess.htm
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<html><head><title>Netlab Reference Manual rbfhess</title></head><body><H1> rbfhess</H1><h2>Purpose</h2>Evaluate the Hessian matrix for RBF network.<p><h2>Synopsis</h2><PRE>h = rbfhess(net, x, t)[h, hdata] = rbfhess(net, x, t)h = rbfhess(net, x, t, hdata)</PRE><p><h2>Description</h2><CODE>h = rbfhess(net, x, t)</CODE> takes an RBF network data structure <CODE>net</CODE>,a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of targetvalues and returns the full Hessian matrix <CODE>h</CODE> corresponding tothe second derivatives of the negative log posterior distribution,evaluated for the current weight and bias values as defined by<CODE>net</CODE>. Currently, the implementation only computes theHessian for the output layer weights.<p><CODE>[h, hdata] = rbfhess(net, x, t)</CODE> returns both the Hessian matrix<CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependentterm in the Hessian.<p><CODE>h = rbfhess(net, x, t, hdata)</CODE> takes a network data structure<CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target values, together with the contribution <CODE>hdata</CODE> arising fromthe data dependent term in the Hessian, and returns the full Hessianmatrix <CODE>h</CODE> corresponding to the second derivatives of the negativelog posterior distribution. This version saves computation time if<CODE>hdata</CODE> has already been evaluated for the current weight and biasvalues.<p><h2>Example</h2>For the standard regression framework with a Gaussian conditionaldistribution of target values given input values, and a simpleGaussian prior over weights, the Hessian takes the form<PRE> h = beta*hdata + alpha*I</PRE><p><h2>See Also</h2><CODE><a href="mlphess.htm">mlphess</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="evidence.htm">evidence</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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