代码搜索：Classify

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h svm_classify.h

// Svm_classify.h: interface for the CSvm_classify class. // ////////////////////////////////////////////////////////////////////// #if !defined(AFX_SVM_CLASSIFY_H__84FFE3EB_1A14_4CD2_918C_FD8E51
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www.eeworm.com/read/302503/13833614

pdf classify1.pdf

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www.eeworm.com/read/301156/13865481

m knn_classify.m

function ret = KNN_Classify(datfile) eval(sprintf('load %s.txt;',datfile)); eval(sprintf('dat=%s; clear %s', datfile, datfile)); looError1 = looknn(dat); ret =1-looError1/size(dat,1)
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www.eeworm.com/read/152843/5664277

h ipt_classify.h

#ifndef _IPT_CLASSIFY_H #define _IPT_CLASSIFY_H struct ipt_classify_target_info { u_int32_t priority; }; #endif /*_IPT_CLASSIFY_H */
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www.eeworm.com/read/152843/5666144

c ipt_classify.c

/* * This is a module which is used for setting the skb->priority field * of an skb for qdisc classification. */ /* (C) 2001-2002 Patrick McHardy * * This program is free softw
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c ipt_classify.c

/* * This is a module which is used for setting the skb->priority field * of an skb for qdisc classification. */ /* (C) 2001-2002 Patrick McHardy * * This program is free softw
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www.eeworm.com/read/476055/6339693

m distance_classify.m

function g=distance_classify(A,b) %距离判别法程序。 %输入已分类样本A（元胞数组），输入待分类样本b %输出待分类样本b的类别g %注：一般还应计算回代误差yita %输入已知分类样本的总类别数n;每类作为元胞数组的一列 n=size(A,1); %输入待分类样本b的样本数m和变量数p [m p]=size(b); for i=1:n
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www.eeworm.com/read/359185/6352529

m start_classify.m

function [D, test_err, train_err] = start_classify(features, targets, error_method, redraws, percent, Preprocessing_algorithm, PreprocessingParameters, Classification_algorithm, AlgorithmParameters, r
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www.eeworm.com/read/493206/6398507

m start_classify.m

function [D, test_err, train_err] = start_classify(features, targets, error_method, redraws, percent, Preprocessing_algorithm, PreprocessingParameters, Classification_algorithm, AlgorithmParameters, r
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www.eeworm.com/read/490185/6460224

m classify_img.m

function [res_img] = classify_img(rgb_img, t_inds,tol); % uses SINGLE cube for background %convert to YCbCr- this has less covariance ycc_img = double(rgb2ycbcr(rgb_img)); [M N P] = size(rgb_img);
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