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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c s_fpclassifyf.c

/* Return classification value corresponding to argument. Copyright (C) 1997, 2000, 2002 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper
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www.eeworm.com/read/124910/6038418

c s_fpclassifyl.c

/* Return classification value corresponding to argument. Copyright (C) 1997, 1999, 2002 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper
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c s_fpclassify.c

/* Return classification value corresponding to argument. Copyright (C) 1997, 2002 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper
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www.eeworm.com/read/124910/6038597

c s_fpclassifyl.c

/* Return classification value corresponding to argument. Copyright (C) 1997, 2002 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper
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www.eeworm.com/read/124910/6038748

c s_fpclassifyl.c

/* Return classification value corresponding to argument. Copyright (C) 1997, 2000, 2002 Free Software Foundation, Inc. This file is part of the GNU C Library. Contributed by Ulrich Drepper
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www.eeworm.com/read/286592/6282713

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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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/493294/6400343

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/490202/6460611

m char3.m

%% Character Recognition Example (III):Training a Simple NN for %% classification %% Read the image I = imread('sample.bmp'); %% Image Preprocessing img = edu_imgpreprocess(I); for cnt = 1:5
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www.eeworm.com/read/489934/6463606

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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