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/299984/7140705
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/461381/7228404
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/460435/7250592
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/460435/7251038
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/460435/7251072
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/460435/7251181
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/456869/7337940
readme
Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
www.eeworm.com/read/450608/7480209
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/450608/7480449
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/450608/7480478
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