%?SVMTRAIN?-?Trains?a?support?vector?machine?incrementally
%????????????using?the?L1?soft?margin?approach?developed?by
%????????????Cauwenberghs?for?two-class?problems.
%
%?Syntax:?[abginduindX_mery_merRsQ]?=?svmtrain（XyCtypescale）
%?????????[abginduindX_mery_merRsQ]?=?svmtrain（XyCtypescaleuind）
%?????????（trains?a?new?SVM?on?the?given?examples）
%
%?????????[abginduindX_mery_merRsQ]?=?svmtrain（XyC）
%?????????[abginduindX_mery_marRsQ]?=?svmtrain（XyCuind）
%?????????（trains?the?current?SVM?in?memory?on?the?given?examples）
%
%??????a:?alpha?coefficients
%??????b:?bias
%??????g:?partial?derivatives?of?cost?function?w.r.t.?alpha?coefficients
%????ind:?cell?array?containing?indices?of?margin?error?and?reserve?vectors
%?????????ind{1}:?indices?of?margin?vectors
%?????????ind{2}:?indices?of?error?vectors
%?????????ind{3}:?indices?of?reserve?vectors
%???uind:?column?vector?of?user-defined?example?indices?（used?for?unlearning?specified?examples）
%??X_mer:?matrix?of?margin?error?and?reserve?vectors?stored?columnwise
%??y_mer:?column?vector?of?class?labels?（-1/+1）?for?margin?error?and?reserve?vectors
%?????Rs:?inverse?of?extended?kernel?matrix?for?margin?vectors
%??????Q:?extended?kernel?matrix?for?all?vectors
%??????X:?matrix?of?training?vectors?stored?columnwise
%??????y:?column?vector?of?class?labels?（-1/+1）?for?training?vectors
%??????C:?soft-margin?regularization?parameter（s）
%?????????dimensionality?of?C???????assumption
%?????????1-dimensional?vector??????universal?regularization?parameter
%?????????2-dimensional?vector??????class-conditional?regularization?parameters?（-1/+1）
%?????????n-dimensional?vector??????regularization?parameter?per?example
%?????????（where?n?=?#?of?examples）
%???type:?kernel?type
%???????????1:?linear?kernel????????K（xy）?=?x‘*y
%?????????2-4:?polynomial?kernel????K（xy）?=?（scale*x‘*y?+?1）^type
%???????????5:?Gaussian?kernel?with?variance?1/（2*scale）
%??scale:?kernel?scale
%
%?Version?3.22e?--?Comments?to?diehl@alumni.cmu.edu
%
clc;
clear;
close?all
%?function?[abginduindXyRsQ]?=?svmtrain（X_newy_newC_newvarargin）
load?banana
x?=?banana;
N?=?200;
X_new?=?x‘;
y_new=[ones（N1）;-ones（N1）];

%%?plot?all?examples
figure;
T.X?=?x‘;
T.y?=?y_new;
ppatterns（T）
%?index?=?[1:100?201:300]
%?T.X?=?x‘;
%?T.y?=?y_new;
%?ppatterns（T）

%?V1=axis;axis（1.2*V1）
%?%獲得增加部分樣本
index1=find（（x（1:2002）<=0）&（x（1:2001）<=0））;%
in1?=?setdiff（1:200index1）;

index2=find（（x（201:4002）<=0）&（x（201:4001）>=-1））;
index2?=?index2+N*ones（size（index21）1）;
in2?=?setdiff（201:400index2）;

addx1?=?x（index1:）;
addx2?=?x（index2:）;

addindex?=?[index1‘index2‘];
?
??in?=?[in1?in2]‘;
??x_ini?=?x（in:）;
??y_ini?=?y_new（in）;
??
??
??
%?flags?for?example?state
MARGIN????=?1;
ERROR?????=?2;
RESERVE???=?3;
UNLEARNED?=?4;

new_model?=?1;?
C_new?=?2;
uind_new?=?zeros（size（y_new））;
type_new?=?5;?
%???????????1:?linear?kernel????

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????7049??2018-09-20?22:58??GY_SVM_Mosek.m
?????文件????????3399??2018-01-19?10:04??plot_svm2.m
?????文件????????2635??2018-01-03?23:55??svm_online.m