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/458392/7297240
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/455967/7360564
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/455967/7360576
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/448038/7541255
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/443673/7629227
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/399996/7816604
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/398337/7993540
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/398337/7993699
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/398324/7994147
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/398324/7994265
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