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/400577/11573220
m reject.m
%REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
www.eeworm.com/read/400577/11573256
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/400577/11573365
m prtestc.m
%PRTESTC Test routine for the PRTOOLS classifier
%
% This script tests a given, untrained classifier w, defined in the
% workspace, e.g. w = my_classifier. The goal is to find out whether
% w fulfill
www.eeworm.com/read/259886/11759585
m demop5.m
%% Normalized Perceptron Rule
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories. Despite the fact that one input vector is much bigger than the
% others, t
www.eeworm.com/read/255755/12057530
m setcost.m
%SETCOST Reset classification cost matrix of dataset
%
% A = SETCOST(A,COST,LABLIST)
%
% The classification cost matrix of the dataset A is reset to COST.
% COST should have size [C,C+n], n >= 0, if
www.eeworm.com/read/255755/12058042
m reject.m
%REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
www.eeworm.com/read/255755/12058104
m setcost.m
%SETCOST Reset classification cost matrix of mapping
%
% W = SETCOST(W,COST,LABLIST)
%
% The classification cost matrix of the dataset W is reset to COST.
% W has to be a trained classifier. CO
www.eeworm.com/read/255755/12058316
m prtestc.m
%PRTESTC Test routine for the PRTOOLS classifier
%
% This script tests a given, untrained classifier w, defined in the
% workspace, e.g. w = my_classifier. The goal is to find out whether
% w fulfill
www.eeworm.com/read/152129/12138201
m classif.m
function classification = classif(Ytrain, Ytest)
% classification = classify(Ytrain, Ytest)
%
% Given the train matrix Ytrain and the test matrix Ytest,
% this function returs a vector classificat
www.eeworm.com/read/150905/12248665
m setcost.m
%SETCOST Reset classification cost matrix of dataset
%
% A = SETCOST(A,COST,LABLIST)
%
% The classification cost matrix of the dataset A is reset to COST.
% COST should have size [C,C+n], n >= 0, if