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/289680/8535103
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/289680/8535166
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/188280/8552239
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/188280/8552317
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/289416/8552837
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/431675/8662460
m testd.m
%TESTD Classification error estimate
%
% [e,j,k,l] = testd(A,W,r,iter)
%
% Test of dataset A on the classifier defined by W. Returns:
% e - the fraction of A that is incorrectly classified by W.
%
www.eeworm.com/read/286662/8751908
m multialgorithms_commands.m
function multialgorithms_commands(command)
%This function processes events from the multi-algorithm GUI screen
switch(command)
case 'Init'
Algorithms = read_algorithms('Classification.tx
www.eeworm.com/read/384922/8834015
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/284759/8900456
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/282846/9056111
m invipsn.m
function A = invIpSN(ntr,m,pye,B)
% find the inverse of the matrix I + Sig*N
%
% S = inv(Sig) + diag(Pi)
% N is the "noise" matrix
% Matlab code for Gaussian Processes for Classification: