代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
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www.eeworm.com/read/413855/2157398
hh ipfilter.hh
#ifndef CLICK_IPFILTER_HH
#define CLICK_IPFILTER_HH
#include "elements/standard/classifier.hh"
#include
CLICK_DECLS
/*
=c
IPFilter(ACTION_1 PATTERN_1, ..., ACTION_N PATTERN_N)
=s
www.eeworm.com/read/429878/8784193
htm knnfwd.htm
Netlab Reference Manual knnfwd
knnfwd
Purpose
Forward propagation through a K-nearest-neighbour classifier.
Synopsis
www.eeworm.com/read/384512/8865993
m knn_old.m
function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old
www.eeworm.com/read/360995/10070194
m dd_error.m
function [e,f] = dd_error(x,w)
%DD_ERROR compute false negative and false positive for oc_classifier
%
% E = DD_ERROR(X,W)
% E = DD_ERROR(X*W)
% E = X*W*DD_ERROR
%
% Compute the fraction of targ
www.eeworm.com/read/451547/7462015
m dd_error.m
function [e,f] = dd_error(x,w)
%DD_ERROR compute false negative and false positive for oc_classifier
%
% E = DD_ERROR(X,W)
% E = DD_ERROR(X*W)
% E = X*W*DD_ERROR
%
% Compute the fraction of targ
www.eeworm.com/read/397115/8066448
m knn_old.m
function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old
www.eeworm.com/read/397111/8067405
m dd_error.m
function [e,f] = dd_error(x,w)
%DD_ERROR compute false positive and false negative for oc_classifier
%
% E = DD_ERROR(X,W)
% E = DD_ERROR(X*W)
% E = X*W*DD_ERROR
%
% Compute the fraction of targ
www.eeworm.com/read/331448/12827264
m knn_old.m
function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old
www.eeworm.com/read/244790/12843518
m knn_old.m
function [Class,P]=knn_old(Data, Proto, proto_class, K)
%KNN_OLD A K-nearest neighbor classifier using Euclidean distance
%
% [Class,P]=knn_old(Data, Proto, proto_class, K)
%
% [sM_class,P]=knn_old
www.eeworm.com/read/143706/12850031
m mcwithsumrule.m
function [Y_compute, Y_prob] = MCWithSumRule(classifier, para, X_train, Y_train, X_test, Y_test, num_class)
class_set = GetClassSet(Y_train);
p = str2num(char(ParseParameter(para, {'-PosNegRatio'