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/415311/11077208
m backpropagation_cgd.m
function [D, Wh, Wo] = Backpropagation_CGD(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm and conjugate gradient descent
www.eeworm.com/read/415311/11077229
m backpropagation_sm.m
function [D, Wh, Wo] = Backpropagation_SM(train_features, train_targets, params, region)
% Classify using a backpropagation network with stochastic learning algorithm with momentum
% Inputs:
% f
www.eeworm.com/read/415311/11077264
m backpropagation_recurrent.m
function [D, Wh, Wo] = Backpropagation_Recurrent(train_features, train_targets, params, region)
% Classify using a backpropagation recurrent network with a batch learning algorithm
% Inputs:
% f
www.eeworm.com/read/415311/11077276
m em.m
function [D, param_struct] = EM(train_features, train_targets, Ngaussians, region)
% Classify using the expectation-maximization algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/415311/11077352
m perceptron_vim.m
function D = Perceptron_VIM(train_features, train_targets, params, region)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/410924/11264764
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
www.eeworm.com/read/191902/8417060
m backpropagation_batch.m
function [D, Wh, Wo] = Backpropagation_Batch(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train
www.eeworm.com/read/191902/8417062
m cascade_correlation.m
function D = Cascade_Correlation(train_features, train_targets, params, region)
% Classify using a backpropagation network with the cascade-correlation algorithm
% Inputs:
% features- Train feat
www.eeworm.com/read/191902/8417066
m nearest_neighbor.m
function D = Nearest_Neighbor(train_features, train_targets, Knn, region)
% Classify using the Nearest neighbor algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Knn
www.eeworm.com/read/191902/8417084
m bayesian_model_comparison.m
function D = Bayesian_Model_Comparison(train_features, train_targets, Ngaussians, region)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
% the maximum nu