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/286592/6282710
m multisvc.m
function [nsv, alpha, b0,t] = multisvc (X,Y,ker,C)
%MULTISVC Support Vector Classification
%
% Usage: [nsv alpha bias] = multisvc(X,Y,ker,C)
%
% Parameters: X - Training inputs
%
www.eeworm.com/read/286592/6282722
asv multisvc.asv
function [nsv, alpha, b0,t] = multisvc (X,Y,ker,C,)
%MULTISVC Support Vector Classification
%
% Usage: [nsv alpha bias] = multisvc(X,Y,ker,C)
%
% Parameters: X - Training inputs
%
www.eeworm.com/read/310630/6343726
m demopnn1.m
%% PNN Classification
% This demonstration uses functions NEWPNN and SIM.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.9 $ $Date: 2002/03/29 19:36:07 $
%%
% Here are three two-e
www.eeworm.com/read/494772/6374955
m cpann_class_param.m
function class_param = cpann_class_param(class_calc,class)
% cpann_class_param calculates classification parameters
% (error rate, non-error rate, specificity, precision and sensitivity)
%
% cla
www.eeworm.com/read/486842/6530683
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
www.eeworm.com/read/484356/6586020
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/484356/6586137
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/483114/6609703
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/405069/11472165
m genetic_programming.m
function [test_targets, best_fun] = genetic_programming(train_patterns, train_targets, test_patterns, params)
% A genetic programming algorithm for classification
%
% train_patterns - Train patt
www.eeworm.com/read/262186/11602314
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