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/349842/10796961
m rce.m
function D = RCE(train_features, train_targets, lambda_m, region)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% features - Train features
% targets - Train targets
% la
www.eeworm.com/read/349842/10796995
m stumps.m
function [D, w] = Stumps(train_features, train_targets, params, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% weights -
www.eeworm.com/read/461621/7223127
m svm.m
function [D, a_star] = SVM(train_features, train_targets, params, region)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
% features- Train f
www.eeworm.com/read/440427/7689477
m cart.m
function test_targets = CART(train_patterns, train_targets, test_patterns, params)
% Classify using classification and regression trees
% Inputs:
% training_patterns - Train patterns
% traini
www.eeworm.com/read/440424/7689525
m backpropagation_stochastic.m
function [test_targets, Wh, Wo, J] = Backpropagation_Stochastic(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic learning algorithm
www.eeworm.com/read/399996/7816626
m deterministic_boltzmann.m
function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% train_pattern
www.eeworm.com/read/399996/7816694
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/399996/7816745
asv svm.asv
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/399996/7816868
m rda.m
function test_targets = RDA (train_patterns, train_targets, test_patterns, lamda)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% train_patterns
www.eeworm.com/read/399996/7816870
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In