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/281455/9154943
m svcinfo.m
function svcinfo(trn,tst,ker,alpha,bias)
%SVCINFO Support Vector Classification Results
%
% Usage: svcinfo(trn,tst,ker,alpha,bias)
%
% Parameters: trn - Training set
% tst - Test
www.eeworm.com/read/183443/9158939
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/183443/9158992
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/181389/9256531
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/181389/9256570
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/181388/9256678
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/181388/9256719
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/177674/9442648
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/177674/9442667
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/176823/9483337
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction