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www.eeworm.com/read/163251/10168519
help recurrent2.help
"recurren2.cpp" Help File
The purpose is to test a
recurrent networks ability to
learn the XOR function and to
function on a KOHEN_SOFM function
and learning rule, as opposed to
the convention
www.eeworm.com/read/354568/10345203
ini default-ini.ini
[Neural Network Parameters]
Initial learning rate (eta) = 0.001
Minimum learning rate (eta) = 0.00005
Rate of decay for learning rate (eta) = 0.794183335 ;;; 0.794183335 = 0.001 down to 0.0000
www.eeworm.com/read/159921/10587903
m contents.m
% Unsupervised statistical learning methods.
%
% unsudemo - Demo of unsupervised learning methods for 2D feature space.
%
% mln - Compute value of logarihm of the likelihood function.
www.eeworm.com/read/159921/10588603
m~ contents.m~
% Statistical Pattern Recognition Toolbox.
%
% Contents
%
% bayes - (dir) Bayes classification.
% datasets - (dir) Functions for handling with data sets.
% generalp - (dir) General purpose
www.eeworm.com/read/422570/10629679
txt todo.txt
-- figure out why the likelihood is positive in the short ap documents
-- fix learning alpha
(a) save alpha
(b) start alpha intelligently, or from the previous value of alpha
(c) fix conver
www.eeworm.com/read/421949/10676594
m contents.m
% Unsupervised statistical learning methods.
%
% unsudemo - Demo of unsupervised learning methods for 2D feature space.
%
% mln - Compute value of logarihm of the likelihood function.