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/410924/11265008
m lms.m
function D = LMS(train_features, train_targets, params, region)
% Classify using the least means square algorithm
% Inputs:
% features - Train features
% targets - Train targets
% param -
www.eeworm.com/read/410924/11265010
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/410924/11265020
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/410924/11265042
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/410924/11265049
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/410924/11265089
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/157703/11670766
m perceptron_bvi.m
function D = Perceptron_BVI(train_features, train_targets, params, region)
% Classify using the batch variable increment Perceptron algorithm
% Inputs:
% features - Train features
% targets
www.eeworm.com/read/157703/11670768
m perceptron_batch.m
function D = Perceptron_Batch(train_features, train_targets, params, region)
% Classify using the batch Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
www.eeworm.com/read/157703/11670778
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/131588/14136152
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