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/demev2.htm | 50 ++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/demev2.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/demev2.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/demev2.htm b/sourcecodes/bnt-master/nethelp3.3/demev2.htm new file mode 100644 index 00000000..c1db9e65 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/demev2.htm @@ -0,0 +1,50 @@ + + + +Netlab Reference Manual demev2 + + + +

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 +