代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/245941/12770871
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/245941/12771097
m rocchiobagging.m
function [test_targets] = RocchioBagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
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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/245941/12771242
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
www.eeworm.com/read/330850/12864713
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
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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 -
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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/330850/12864775
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/330850/12864778
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