代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
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www.eeworm.com/read/413912/11137602
m svm_learn.m
function status = svm_learn(options, examples, model)
% SVM_LEARN - Interface to SVM light, learning module
%
% STATUS = SVM_LEARN(OPTIONS, EXAMPLES, MODEL)
% Call the training program 'svm_learn
www.eeworm.com/read/248950/12534089
m svm_learn.m
function status = svm_learn(options, examples, model)
% SVM_LEARN - Interface to SVM light, learning module
%
% STATUS = SVM_LEARN(OPTIONS, EXAMPLES, MODEL)
% Call the training program 'svm_learn
www.eeworm.com/read/133942/14017162
m nefrule2.m
function nefrule2(fuzzy_error, input_stack, nef_rule);
%NEFRULES Rule learning function (Phase2)
% This function learns the rules of the
% fismatrix by using the fuzzy_error and the c
www.eeworm.com/read/187173/8848973
html index.html
Lesson: Learning Swing by Example (The Java™ Tutorials > Creating a GUI with JFC/Swing)
www.eeworm.com/read/184617/9091722
m svmnlspex01.m
%SVMnLSPex01.m
%
%Two Dimension SVM Problem, Two Class Un-separable Situation
%
%Method from Thorsten Joachims:
%"Making Large-Scale SVM Learning Practical", function (4)/(5)/(6)
%
% Objecti
www.eeworm.com/read/362008/10024014
m rbf_network.m
function [test_targets, mu, Wo] = RBF_Network(train_patterns, train_targets, test_patterns, Nh)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% train_patte
www.eeworm.com/read/353334/10453965
m hdda_learn.m
function prms = hdda_learn(Xl,varargin);
% High Dimensional Discriminant Analysis (learning)
%
% Usage: (1) prms = hdda_learn(X,'model','best','seuil',s);
% (1) prms = hdda_learn(X,'model',
www.eeworm.com/read/160933/10469269
m svcm_train.m
function [a, b, g, inds, inde, indw] = svcm_train(x, y, C);
% function [a, b, g, inds, inde, indw] = svcm_train(x, y, C);
% support vector classification machine
% incremental learning,