代码搜索：classification

找到约 3,679 项符合「classification」的源代码

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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/130490/14190144

xml tutorial.xml

Tutorial This short tutorial shows how to use Select for doing some email classification testing. It does not show, however, h
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www.eeworm.com/read/128193/14311493

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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www.eeworm.com/read/128193/14311530

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!) % %
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www.eeworm.com/read/225111/14555596

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/223346/14644060

m onevsalltrain.m

function [alpha,bias,svi,nsv] = onevsalltrain(samples,labels,kernel,kernelparam,lamda,epsilon,func) % multiclass classification using one-against-all % training function nlabels = max(labels);
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www.eeworm.com/read/223346/14644068

m onevsalltest.m

function [result,vote] = onevsalltest(testsamples,testlabels,samples,labels,kernel,kernelparam,alpha,svi,bias,func) % multiclass classification using one-against-all % test function nlabels = m
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www.eeworm.com/read/122800/14668034

c em.c

/* Weight-setting and scoring implementation for EM classification */ /* Copyright (C) 1997, 1998, 1999 Andrew McCallum Written by: Kamal Nigam This file is part of the B
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www.eeworm.com/read/222301/14697806

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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www.eeworm.com/read/222301/14697852

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!) % %
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