From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/nethelp3.3/mlphess.htm | 73 +++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/mlphess.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/mlphess.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/mlphess.htm b/sourcecodes/bnt-master/nethelp3.3/mlphess.htm new file mode 100644 index 00000000..59a6ec91 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mlphess.htm @@ -0,0 +1,73 @@ + + + +Netlab Reference Manual mlphess + + + +

mlphess +

+Purpose +

+Evaluate the Hessian matrix for a multi-layer perceptron network. + +

+Synopsis +

+h = mlphess(net, x, t)
+[h, hdata] = mlphess(net, x, t)
+h = mlphess(net, x, t, hdata)
+

+ + +

+Description +

+h = mlphess(net, x, t) takes an MLP network data structure net, +a matrix x of input values, and a matrix t of target +values and returns the full Hessian matrix h corresponding to +the second derivatives of the negative log posterior distribution, +evaluated for the current weight and bias values as defined by +net. + +

[h, hdata] = mlphess(net, x, t) returns both the Hessian matrix +h and the contribution hdata arising from the data dependent +term in the Hessian. + +

h = mlphess(net, x, t, hdata) takes a network data structure +net, a matrix x of input values, and a matrix t of +target values, together with the contribution hdata arising from +the data dependent term in the Hessian, and returns the full Hessian +matrix h corresponding to the second derivatives of the negative +log posterior distribution. This version saves computation time if +hdata has already been evaluated for the current weight and bias +values. + +

+Example +

+For the standard regression framework with a Gaussian conditional +distribution of target values given input values, and a simple +Gaussian prior over weights, the Hessian takes the form +

+
+    h = beta*hd + alpha*I
+

+ +where the contribution hd is evaluated by calls to mlphdotv and +h is the full Hessian. + +

+See Also +

+mlp, hesschek, mlphdotv, evidence

+Pages: +Index +