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/240162/4588592
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/233448/4682580
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/292144/3958762
m4 hipe_bif_list.m4
/* $Id$
*
* List all non architecture-specific BIFs and primops, and
* classify each as belonging to one of the classes below.
* This list is included in hipe_${ARCH}_bifs.m4, which is
* responsi
www.eeworm.com/read/429426/1949353
old entries.extra.old
D/Associate////
D/Classify////
D/Data////
D/Evaluate////
D/Other////
D/Visualize////
D/icons////
/OWOptions.py///1052316423/
/OWToolbars.py///1098691012/
/OWWidget.py///1127830174/
/ColorPal
www.eeworm.com/read/386597/2570099
m classification_error.m
function [classify, err] = classification_error(D, patterns, targets, region)
%Find a classification error for a given decision surface D and a given set of
%patterns (2xL) and targets (1xL)
%The
www.eeworm.com/read/386597/2570104
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/386597/2570124
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/386597/2570173
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/386597/2570174
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/386597/2570178
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