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m backpropagation_stochastic.m

function [test_targets, Wh, Wo, J] = Backpropagation_Stochastic(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation network with stochastic learning algorithm
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m backpropagation_recurrent.m

function [test_targets, W, J] = Backpropagation_Recurrent(train_patterns, train_targets, test_patterns, params) % Classify using a backpropagation recurrent network with a batch learning algorithm
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www.eeworm.com/read/415313/11076735

m train_test_multiple_class_al.m

% Train_Test_Multiple_Class_AL: multi-class active learning wrapper for binary % classifiers % % Pararmeters: % classifier: the base classifier % para: parameters % 1. CodeType: multi-class c
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www.eeworm.com/read/415311/11077081

m optimal_brain_surgeon.m

function [D, Wh, Wo] = Optimal_Brain_Surgeon(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm and remove excess units % usi
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www.eeworm.com/read/414357/11119339

m learn_marq.m

function jac = learn_marq(p,d) %LEARN_MARQ Marquardt Backpropagation Learning Rule % % (See PURELIN, LOGSIG, TANSIG) % % jac = LEARN-MARQ(P,D) % P - RxQ ma
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www.eeworm.com/read/268797/11121197

readme

This is a complete rewrite of the Korn Shell debugger from Bill Rosenblatt's `Learning the Korn Shell', published by O'Reilly and Associates (ISBN 1-56592-054-6). Michael Loukides and Cigy Cyriac made
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www.eeworm.com/read/147693/12538604

pl fig19_8.pl

% Figure 19.8 Learning about odd-length and even-length simultaneously. % Inducing odd and even length for lists backliteral( even( L), [ L:list], []). backliteral( odd( L), [ L:list], []).
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www.eeworm.com/read/133942/14017222

m nefrules.m

function nefrules(fuzzy_error, input_stack, nef_rule); %NEFRULES Rule learning function (Phase1) % This function learns the rules of the % fismatrix by using the fuzzy_error and the c
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www.eeworm.com/read/113579/15452998

m get_data.m

function [X, y, conf] = get_data(conf, field) % % Get training or test data for a simulated learning problem. % % The input data is returned in X, the output in y. Optional % parameters are controlled
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prm qagent.prm

# # Configuration parameters for JObjects QuestAgent applet # # Basic parameters... IndexFile1=data/index.que IndexDescription1=Learning Perl Prefix1=../ # Some custom settings... layou
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