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/380709/2652905
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/373460/2761803
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/261925/4319303
py __init__.py
"""Package for interacting with VM folders.
Design notes go here.
Use ZODB to store training data and classifier.
The spam and ham data are culled from sets of folders. The actual
tokenized messag
www.eeworm.com/read/471381/6892022
m singleweaklearner.m
function [H]=SingleWeakLearner(X,Y,C,W)
% Train a weak classifier wrt ONE feature given in C
% Use 2-class Gaussian model:
%
% Input
% X - samples
% Y - label of samples -
% 1
www.eeworm.com/read/293183/8310317
m parsc.m
%PARSC Pars classifier
%
% parsc(w)
%
% Displays the type and, for combining classifiers, the structure of
% the mapping w.
%
% See also mappings
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
www.eeworm.com/read/293183/8310596
m roc.m
%ROC Receiver-operator curve
%
% e = roc(D,k)
%
% Computes k points of the receiver-operator curve of the classifier
% W for the labeled data set D, which is typically the result of
% D = A*W*clas
www.eeworm.com/read/293183/8310694
m reject.m
%REJECT Compute error-reject trade-off curve
%
% e = reject(D)
%
% Computes the error-reject curve of the classification result
% D = A*W, in which A is a dataset and W a classifier. e is a
% set
www.eeworm.com/read/367442/9747772
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/266608/11217055
readme
WLSVM is a custom Weka classifier designed to give Weka users an easy access
to the effecient and fast SVM tool, Libsvm. WLSVM runs much faster
than Weka SMO class and suports all Libsvm features
www.eeworm.com/read/411674/11233769
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