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/328782/3436193
h globaltestdata_marki.h
// {{{RME classifier 'Logical View::TestHarnesses::MarkI_Tests::GlobalTestData_MarkI'
#ifndef rtg_GlobalTestData_MarkI_H
#define rtg_GlobalTestData_MarkI_H
#ifdef PRAGMA
#pragma interface "rtg/Globa
www.eeworm.com/read/328782/3436200
h coffeepotnotempty_test.h
// {{{RME classifier 'Logical View::TestHarnesses::MarkI_Tests::Scenarios_MarkI::CoffeePotNotEmpty_Test'
#ifndef rtg_CoffeePotNotEmpty_Test_H
#define rtg_CoffeePotNotEmpty_Test_H
#ifdef PRAGMA
#prag
www.eeworm.com/read/317451/3579884
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 hash
www.eeworm.com/read/449253/1678806
java estimator.java
/**
* @(#)Estimator.java 1.5.0 09/01/18
*/
package ml.classifier.dt;
/**
* An estimator specifically used to estimate extra error ratio for c4.5's error-based pruning strategy.
*
www.eeworm.com/read/429717/1946266
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 hash
www.eeworm.com/read/429426/1948668
txt hhstructure.txt
Orange Modules ---> default.htm
Association Rules ---> orngAssoc.htm
Bayesian Classifier ---> orngBayes.htm
C4.5 ---> orngC45.htm
Constructive induction ---> orngCI.htm
Rule Learning ---> or
www.eeworm.com/read/414826/2141350
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 hash
www.eeworm.com/read/411379/2188968
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/411379/2189007
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/411379/2189010
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%