代码搜索：classification

找到约 3,679 项符合「classification」的源代码

代码结果 3,679
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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:
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www.eeworm.com/read/455967/7360567

asv svcinfo.asv

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
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www.eeworm.com/read/455967/7360615

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
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www.eeworm.com/read/455917/7361835

m mindismain.m

function [errorrate,result]=MINDISmain() % this function is used to simulate the process of minimum distance % classification data=zeros(56*46*400,1); fid=fopen('facedata','r'); [data,count]=fr
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www.eeworm.com/read/450608/7480447

m fdsc.m

%FDSC Feature based Dissimilarity Space Classification % % W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF) % W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF) % % INPUT % A Dateset used for training % R
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www.eeworm.com/read/439518/7706970

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:
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www.eeworm.com/read/439513/7707448

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:
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www.eeworm.com/read/438780/7727118

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
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www.eeworm.com/read/399996/7816938

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
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www.eeworm.com/read/398324/7994398

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
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