代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/298374/7964824
m c4_5testfun.m
%C4_5TestFun.m
%Shiliang Sun (shiliangsun@gmail.com), Apr. 8, 2007
%Using the learned 4.5 decision tree to classify samples
%This code is based on the C4_5.m file from "Classification Toolbox for M
www.eeworm.com/read/298374/7964831
m c4_5trainfun.m
%C4_5TrainFun.m
%Shiliang Sun (shiliangsun@gmail.com), Apr. 8, 2007
%Learning a decision tree by the C4.5 algorithm
%This code is based on the C4_5.m file from "Classification Toolbox for Matlab"
www.eeworm.com/read/312163/13617098
html references.html
References
[Anderson62]
T.W.Anderson and R.R.Bahadur.
Classification into two multivariate normal distributions with differrentia
covariance mat
www.eeworm.com/read/150760/12265007
html references.html
References
[Anderson62]
T.W.Anderson and R.R.Bahadur.
Classification into two multivariate normal distributions with differrentia
covariance mat
www.eeworm.com/read/213492/15133340
html references.html
References
[Anderson62]
T.W.Anderson and R.R.Bahadur.
Classification into two multivariate normal distributions with differrentia
covariance mat
www.eeworm.com/read/413855/2158240
hh meter.hh
#ifndef CLICK_METER_HH
#define CLICK_METER_HH
#include "bandwidthmeter.hh"
CLICK_DECLS
/*
* =c
* Meter(RATE1, RATE2, ..., RATEI)
* =s classification
* classifies packet stream by rate (pkt/s)
www.eeworm.com/read/192735/8289314
m svm_multi_pred.m
function [beta, bo] = svm_multi_pred(X,Y,C,varargin)
% SVM_MULTI_PRED
%
% Support Vector Multi Classification
%
% USAGE: [beta, bo] = svm_multi_pred(X,Y,C,vargin)
%
% PARAMETERS: X - (m,d) matri
www.eeworm.com/read/411674/11233164
html references.html
References
[Anderson62]
T.W.Anderson and R.R.Bahadur.
Classification into two multivariate normal distributions with differrentia
covariance mat
www.eeworm.com/read/303463/3810308
m display.m
function display(X)
%display Overloaded
% Author Johan L鰂berg
% $Id: display.m,v 1.2 2007/02/02 09:31:24 joloef Exp $
P = X.P;
classification = 'Logdet-term ';
[n,m] = size(P);