代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/357874/10199052
m backpropagation_batch.m
function [test_targets, Wh, Wo, J] = Backpropagation_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs
www.eeworm.com/read/357874/10199058
m perceptron_bvi.m
function [test_targets, a] = Perceptron_BVI(train_patterns, train_targets, test_patterns, params)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/357874/10199062
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/357874/10199069
m backpropagation_quickprop.m
function [test_targets, Wh, Wo, J] = Backpropagation_Quickprop(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and q
www.eeworm.com/read/357874/10199070
m minimum_cost.m
function test_targets = Minimum_Cost(train_patterns, train_targets, test_patterns, lambda)
% Classify using the minimum error criterion via histogram estimation of the densities
% Inputs:
% trai
www.eeworm.com/read/357874/10199081
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/357874/10199088
m perceptron_fm.m
function [test_targets, a] = Perceptron_FM(train_patterns, train_targets, test_patterns, params)
% Classify using the Perceptron algorithm but at each iteration updating the worst-classified sample
www.eeworm.com/read/357874/10199097
m perceptron.m
function [test_targets, a] = Perceptron(train_patterns, train_targets, test_patterns, alg_param)
% Classify using the Perceptron algorithm (Fixed increment single-sample perceptron)
% Inputs:
%
www.eeworm.com/read/357874/10199158
m backpropagation_cgd.m
function [test_targets, Wh, Wo, errors] = Backpropagation_CGD(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and co
www.eeworm.com/read/357874/10199179
m backpropagation_sm.m
function [test_targets, Wh, Wo, J] = Backpropagation_SM(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic learning algorithm with mome