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/157890/11657720
m gasenevalc.m
function [sol,val] = gasenEvalC(sol,options)
%
% fitness function used by GASEN for classification
%
% to use this function, GAOT toolbox must be available. refer: C.R. Houck, J.A. Joines, and M.G
www.eeworm.com/read/157074/11741311
m exmultikernellarclass.m
%
% Example of KBP applied on a classification problem
%
% 20/12/05 AR
clear all
close all
n = 500;
sigma=0.4;
[xapp,yapp,xtest,ytest]=dataset('checkers',n,0,sigma);
[xapp]=normalizemeanstd(xap
www.eeworm.com/read/259886/11759872
m demopnn1.m
%% PNN Classification
% This demonstration uses functions NEWPNN and SIM.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.9 $ $Date: 2002/04/14 21:28:08 $
%%
% Here are three two-e
www.eeworm.com/read/155041/11902207
m svcm_test.m
function [ypred,indw] = svcm_test(xtest,ytest,xtrain,ytrain,atrain,btrain);
% function [ypred,indw] = svcm_test(xtest,ytest,xtrain,ytrain,atrain,btrain);
%
% support vector classification machine
% te
www.eeworm.com/read/256399/12001630
m exnuclass1.m
%
% SVM Classification 2D examples
% with different kernels (including wavelets) and different penalization settings
%
% 05/05/03 AR
clear all
close all
n = 100;
sigma=0.4;
[Xapp,yapp,xtest,yt
www.eeworm.com/read/256398/12001780
m exmultikernellarclass.m
%
% Example of KBP applied on a classification problem
%
% 20/12/05 AR
clear all
close all
n = 500;
sigma=0.4;
[xapp,yapp,xtest,ytest]=dataset('checkers',n,0,sigma);
[xapp]=normalizemeanstd(xap
www.eeworm.com/read/152779/12085716
m exnuclass1.m
%
% SVM Classification 2D examples
% with different kernels (including wavelets) and different penalization settings
%
% 05/05/03 AR
clear all
close all
n = 100;
sigma=0.4;
[Xapp,yapp,xtest,yt
www.eeworm.com/read/130491/14189818
1 dbacl.1
\" t
.TH DBACL 1 "Bayesian Text Classification Tools" "Version 1.3" ""
.SH NAME
dbacl \- a digramic Bayesian classifier for text recognition.
.SH SYNOPSIS
.HP
.B dbacl
[-dvnirMND]
[-T
.IR type
] -l
www.eeworm.com/read/128193/14311431
m maxwin.m
function net = maxwin(arg, sv, w, bias, C, zeta)
% MAXWIN
%
% Construct a max-win multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class maxw
www.eeworm.com/read/122800/14667872
news
* New since libbow version 0.9
New classification methods have been added: maxent, svm, active,
nbshrinkage.
New libbow front-ends have been released: crossbow (document
clustering), archer (Alta