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/418755/10928180
m demo.m
%
% DEMONSTRATION OF ADABOOST_tr and ADABOOST_te
%
% Just type "demo" to run the demo.
%
% Using adaboost with linear threshold classifier
% for a two class classification problem.
%
% Bug Reporting:
www.eeworm.com/read/273055/10930194
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/273047/10930342
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/418695/10935744
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/418459/10944651
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/417431/10990122
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/417218/10999820
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/470924/6901773
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/469416/6976484
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 predi
www.eeworm.com/read/469416/6976496
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 w