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www.eeworm.com/read/299459/7850386
m~ svm2.m~
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/299459/7850414
m svm2.m
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/198177/7948200
m unc_n1_sin1.m
function [fval]=unc_n1_sin1(x)
%reference:
%note that you can get the formulation of unc_n1_sin1 from some
%aritcles,such as
%(1)LN de Castro, FJ Von Zuben 'Learning and optimization using the clo
www.eeworm.com/read/198177/7948449
m unc_n2_sin1.m
function [fval]=unc_n2_sin1(x)
%reference:
%note that you can get the formulation of unc_n2_sin1 from some
%aritcles,such as
%(1)LN de Castro, FJ Von Zuben 'Learning and optimization using the clo
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/396876/8086350
m demo_bp2.m
% nnd_bp2.m
% 8-3-8 Ecoder Problem
% Backprogation with adaptive learning rate
%
% Matlab Neural Network Toolbox 3.0
% O. Bittel, 1.3.1999
echo on
% Lernaufgabe
P = [1 0 0 0 0 0 0 0;
www.eeworm.com/read/312163/13617428
m~ svm2.m~
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le
www.eeworm.com/read/312163/13617434
m svm2.m
function model = svm2(data,options)
% SVM2 Learning of binary SVM classifier with L2-soft margin.
%
% Synopsis:
% model = svm2(data)
% model = svm2(data,options)
%
% Description:
% This function le