代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/192203/8399614
m example_classification.m
% Example: RF for Classification
% Run RF on Training set
traindata = textread('satimage_tra.txt');
x = traindata(:,1:(end-1));
y = traindata(:,end);
y(y==7) = 6;
cat = one
www.eeworm.com/read/191902/8417046
pdf classification_toolbox.pdf
www.eeworm.com/read/191902/8417072
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/286662/8751666
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/177129/9468761
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The
www.eeworm.com/read/372113/9521096
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/362008/10023786
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/357874/10199057
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/349842/10796657
m classification_error.m
function [classify, err] = classification_error(D, features, targets, region)
%Find a classification error for a given decision surface D and a given set of
%features (2xL) and targets (1xL)
%The