代码搜索：Classifier
找到约 4,824 项符合「Classifier」的源代码
代码结果 4,824
www.eeworm.com/read/301403/3840214
tcl flowmon.tcl
# make a flow monitor
proc makeflowmon {} {
global ns
set flowmon [new QueueMonitor/ED/Flowmon]
set cl [new Classifier/Hash/SrcDestFid 33]
$cl proc unknown-flow { src dst fid } {
www.eeworm.com/read/292705/3950091
c cls_u32.c
/*
* net/sched/cls_u32.c Ugly (or Universal) 32bit key Packet Classifier.
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public
www.eeworm.com/read/290228/3983399
tcl flowmon.tcl
# make a flow monitor
proc makeflowmon {} {
global ns
set flowmon [new QueueMonitor/ED/Flowmon]
set cl [new Classifier/Hash/SrcDestFid 33]
$cl proc unknown-flow { src dst fid } {
www.eeworm.com/read/439082/1820106
c cls_u32.c
/*
* net/sched/cls_u32.c Ugly (or Universal) 32bit key Packet Classifier.
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public
www.eeworm.com/read/430518/1928973
c cls_u32.c
/*
* net/sched/cls_u32.c Ugly (or Universal) 32bit key Packet Classifier.
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public
www.eeworm.com/read/430506/1929369
m~ knnclass.m~
function [class] = knnclass(tst,X,I,K)
% [class] = knnclass(tst,X,I,K)
%
% KNNCLASS is an implementation of K-Nearest Neighbours
% classifier. The Euclidean distance is used.
%
% Input:
% tst [DxNt
www.eeworm.com/read/429426/1949339
py owclassificationtree.py
"""
Classification Tree
Classification tree learner/classifier.
icons/ClassificationTree.png
Janez Demsar (janez.demsar(@at@)fri.uni-l
www.eeworm.com/read/428780/1954092
m mvsvmclass.m
function [y,votes] = mvsvmclass(X,model)
% MVSVMCLASS Majority voting multi-class SVM classifier.
%
% Synopsis:
% [y,votes] = mvsvmclass(X,model)
%
% Description:
% [y,votes] = mvsvmclass(X,model) m
www.eeworm.com/read/428780/1954186
m knnrule.m
function model=knnrule(data,K)
% KNNRULE Creates K-nearest neighbours classifier.
%
% Synopsis:
% model=knnrule(data)
% model=knnrule(data,K)
%
% Description:
% It creates model of the K-nearest ne
www.eeworm.com/read/428780/1954188
m knnclass.m
function y = knnclass(X,model)
% KNNCLASS k-Nearest Neighbours classifier.
%
% Synopsis:
% y = knnclass(X,model)
%
% Description:
% The input feature vectors X are classified using the K-NN
% rule