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/386597/2570195
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/386597/2570208
m backpropagation_recurrent.m
function [test_targets, W, J] = Backpropagation_Recurrent(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation recurrent network with a batch learning algorithm
www.eeworm.com/read/366702/2878925
c 20020810-1.c
/* PR target/7559
This testcase was miscompiled on x86-64, because classify_argument
wrongly computed the offset of nested structure fields. */
extern void abort (void);
struct A
{
long x;
www.eeworm.com/read/474600/6813419
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/474600/6813440
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/474600/6813454
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/474600/6813496
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/474600/6813497
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
www.eeworm.com/read/474600/6813504
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/474600/6813525
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