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/317622/13500831
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/317622/13500842
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
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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/317622/13500858
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:
%
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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/317622/13500940
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
www.eeworm.com/read/317622/13500974
m perceptron_vim.m
function [test_targets, a] = Perceptron_VIM(train_patterns, train_targets, test_patterns, params)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% train_pat
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m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/316604/13520398
m discrete_bayes.m
function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature)
% Classify discrete features using the Bayes decision theory
% Inputs:
% features - Train features
% targ
www.eeworm.com/read/316604/13520517
m rbf_network.m
function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train features
% t