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/399996/7817079
asv ada_boost.asv
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/399996/7817116
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/197100/8029616
am makefile.am
# TODO(luc) Add 'doc' to this list when ready
SUBDIRS = ccstruct ccutil classify cutil dict display image textord viewer wordrec ccmain training
EXTRA_DIST = tessdata phototest.tif tesseract.dsp tess
www.eeworm.com/read/397099/8068717
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/397099/8068738
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t
www.eeworm.com/read/397099/8068809
m ada_boost.m
function [test_targets, E] = ada_boost(train_patterns, train_targets, test_patterns, params)
% Classify using the AdaBoost algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/397099/8068915
m local_polynomial.m
function test_targets = Local_Polynomial(train_patterns, train_targets, test_patterns, Nlp)
% Classify using the local polynomial fitting
% Inputs:
% train_patterns - Train patterns
% train_tar
www.eeworm.com/read/397099/8069086
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/245941/12770726
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/245941/12770752
m nearest_neighbor.m
function test_targets = Nearest_Neighbor(train_patterns, train_targets, test_patterns, Knn)
% Classify using the Nearest neighbor algorithm
% Inputs:
% train_patterns - Train patterns
% train_t