代码搜索：classification

找到约 3,679 项符合「classification」的源代码

代码结果 3,679
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/483114/6609798

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/407916/11408566

changelog

MultiBoost 0.71: NEW: Added output of the classification prediction. FIXED: Bad bug which made the -d option useless. CHANGED: Now multiple declarations of the same argument (with a different numbe
·
www.eeworm.com/read/405069/11472265

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/347811/11635088

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/157733/11667600

c kl.c

/* Weight-setting and scoring for Kuback-Leiber classification */ /* Copyright (C) 1997 Andrew McCallum Written by: Andrew Kachites McCallum This file is part of the Ba
·
www.eeworm.com/read/157733/11667772

c evi.c

/* Weight-setting and scoring for P(C|w) evidence classification */ /* Copyright (C) 1997 Andrew McCallum Written by: Andrew Kachites McCallum This file is part of the
·
www.eeworm.com/read/157733/11667785

c naivebayes.c

/* Weight-setting and scoring implementation for Naive-Bayes classification */ /* Copyright (C) 1997 Andrew McCallum Written by: Andrew Kachites McCallum This file is p
·
www.eeworm.com/read/154122/11988705

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/342008/12047691

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/255755/12058037

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
·