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/397099/8069012
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
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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 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
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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
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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
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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
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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