Topic 7 Support Vector Machine for Classification.

Topic 7Topic 7Support Vector Machine for Support Vector Machine for

ClassificationClassification

Outline

Linear Maximal Margin Classifier for Linearly Separable Data

Linear Soft Margin Classifier for Overlapping Classes

The Nonlinear Classifier

Linear Maximal Margin Classifier for linearly Separable Data

Goal: seeking an optimal separating plane.– That is, among all the hyperplanes that minimizes th

e training error (empirical risk), find the one with the largest margin.

A classifier with a larger margin might have better performance in generalization; on the other hand, a classifier with a smaller margin might have a higher expected risk.


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1. Minimize the training error

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maximize margin → minimize wTw

2. Maximize the margin

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Pattern= Target= norm = [1 1 1 [ 1.4142 1 2 1 2.2361 2 -1 1 2.2361 2 0 1 2.0000 -1 2 -1 2.2361 -2 1 -1 2.2361 -1 -1 -1 1.4142 -2 -2 -1 ] 2.8284 ] -> R

•K=[ 2 3 1 2 1 -1 -2 -4 3 5 0 2 3 0 -3 -6 1 0 5 4 -4 -5 -1 -2 2 2 4 4 -2 -4 -2 -4 1 3 -4 -2 5 4 -1 -2 -1 0 -5 -4 4 5 1 2 -2 -3 -1 -2 -1 1 2 4 -4 -6 -2 -4 -2 2 4 8 ]

1st iteration α=[0 0 0 0 0 0 0 0], b=0, R=2.8284 x1=[1 1]; y1=1; k(:,1)=[2 3 1 2 1 -1 -2 -4]

X2=[1 2]; y2=1; k(:,2)=[3 5 0 2 3 0 -3 -6]

X3=[2 -1];y3=1;k(:,3)=[1 0 5 4 -4 -5 -1 -2] 1*[1*1+8]=9>0 X4=[2 0];y4=1; k(:,4)=[2 2 4 4 -2 -4 -2 -4] 1*[1*2+8]=10>0

88284.2*8284.2*10b 0], 0 0 0 0 0 0 [1

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1st iteration

X5=[-1 2];y5=-1;k(:,5)=[1 3 -4 -2 5 4 -1 -2]

(-1)*[1*1+8]= -9

α=[1 0 0 0 1 0 0 0], b=8-8=0 X6=[-2 1];y6=-1;k(:,6)=[-1 0 -5 -4 4 5 1 2]

(-1)*[1*(-1)+(-1)*4+0]= 5>0 X7=[-1 -1];y7=-1;k(:,7)=[-2 -3 -1 -2 -1 1 2 4]

(-1)*[1*(-2)+(-1)*(-1)+0]=1>0 X8=[-2 -2];y8=-1;k(:,8)=[-4 -6 -2 -4 -2 2 4 8]

(-1)*[1*(-4)+(-1)*(-2)+0]=2>0

2ed iteration α=[1 0 0 0 1 0 0 0], b=0, R=2.8284 x1=[1 1]; y1=1; k(:,1)=[2 3 1 2 1 -1 -2 -4] 1*[1*2+(-1)*1+0]=1>0 X2=[1 2]; y2=1; k(:,2)=[3 5 0 2 3 0 -3 -6] 1*[1*3+(-1)*3+0]=0 α=[1 1 0 0 1 0 0 0], b=0+8=8 X3=[2 -1];y3=1;k(:,3)=[1 0 5 4 -4 -5 -1 -2] 1*[1*1+1*0+(-1)*(-4)+8]=14>0 X4=[2 0];y4=1; k(:,4)=[2 2 4 4 -2 -4 -2 -4] 1*[1*2+1*2+(-1)*(-2)+8]=14>0

2ed iteration

X5=[-1 2];y5=-1;k(:,5)=[1 3 -4 -2 5 4 -1 -2]

(-1)*[1*1+1*3+(-1)*5+8]= -7

α=[1 1 0 0 2 0 0 0], b=8-8=0 X6=[-2 1];y6=-1;k(:,6)=[-1 0 -5 -4 4 5 1 2]

(-1)*[1*(-1)+1*0+(-2)*4+0]=9>0 X7=[-1 -1];y7=-1;k(:,7)=[-2 -3 -1 -2 -1 1 2 4]

(-1)*[1*(-2)+1*(-3)+(-2)*(-1)+0]=3>0 X8=[-2 -2];y8=-1;k(:,8)=[-4 -6 -2 -4 -2 2 4 8]

(-1)*[1*(-4)+1*(-6)+(-2)*(-2)+0]=6>0

3rd iteration α=[1 1 0 0 2 0 0 0], b=0, R=2.8284 x1=[1 1]; y1=1; k(:,1)=[2 3 1 2 1 -1 -2 -4] 1*[1*2+1*3+(-2)*1+0]=3>0 X2=[1 2]; y2=1; k(:,2)=[3 5 0 2 3 0 -3 -6] 1*[1*3+1*(5)+(-2)*3+0]=2>0 X3=[2 -1];y3=1;k(:,3)=[1 0 5 4 -4 -5 -1 -2] 1*[1*1+1*0+(-2)*(-4)+0]=9>0 X4=[2 0];y4=1; k(:,4)=[2 2 4 4 -2 -4 -2 -4] 1*[1*2+1*2+(-2)*(-2)+0]=8>0 X5=[-1 2];y5=-1;k(:,5)=[1 3 -4 -2 5 4 -1 -2]

(-1)*[1*1+1*3+(-2)*5+0]= 6>0 X6=[-2 1];y6=-1;k(:,6)=[-1 0 -5 -4 4 5 1 2]

(-1)*[1*(-1)+1*0+(-2)*4+0]=9>0 X7=[-1 -1];y7=-1;k(:,7)=[-2 -3 -1 -2 -1 1 2 4]

(-1)*[1*(-2)+1*(-3)+(-2)*(-1)+0]=3>0 X8=[-2 -2];y8=-1;k(:,8)=[-4 -6 -2 -4 -2 2 4 8]

(-1)*[1*(-4)+1*(-6)+(-2)*(-2)+0]=6>0

f(x)=sum(z.*y.*k(x,x)')+b=1*(1*x1+1*x2)+1*(1*x1+2*x2)+2*(-1*x1+2*x2)+0=7x2



Linear Soft Margin Classifier for Overlapping Classes

Soft margin

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2-parameter Sequential Minimal Optimization Algorithm

At every step, SMO chooses two Lagrange multiplier to jointly optimize, finds the optimal values for these multipliers, and updates the SVM to reflect the new optimal values.

Heuristic to choose which multipliers to optimize– first multiplier is the multiplier of the pattern with

the largest current prediction error– Second multiplier is the multiplier of the pattern

with the smallest current prediction error

})({max iii

yxf

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yxf

Step 1. Choose 2 multiplier2 α1 and α2

Step 2. Define bounds for α2

If y1≠y2,

If y1=y2,

Step 3. Update α2

Step 4. Update α1

})({min};)({max 21 iii

iii

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K=[ 2 3 1 1 2 1 -1 0 -2 -4 3 5 1 0 2 3 0 0 -3 -6 1 1 1 2 2 -1 -2 0 -1 -2 1 0 2 5 4 -4 -5 0 -1 -2 2 2 2 4 4 -2 -4 0 -2 -4 1 3 -1 -4 -2 5 4 0 -1 -2 -1 0 -2 -5 -4 4 5 0 1 2 0 0 0 0 0 0 0 0 0 0 -2 -3 -1 -1 -2 -1 1 0 2 4 -4 -6 -2 -2 -4 -2 2 0 4 8]

Pattern= [1 1; 1 2; 1 0; 2 -1; 2 0; -1 2; -2 1; 0 0; -1 -1; -2 -2]

Target= [ 1; 1; -1; 1; 1; -1; -1; 1; -1; -1 ]

C=0.8

1st iteration

F(x)-Y=[0 -1.4 3.4 7.1 5.7 -8 -10.9 -2.9 -3.8 -6.7]’ α=[0.8 0 0 0.8 0 0.3 0.8 0 0 0]‘ b=1-(0.8*1*2+0.8*1*1+0.3*(-1)*1+0.8*(-1)*(-1))

=1-2.9= -1.9 f(x)=sum(z.*y.*k(x,x)')+b=0.8*(1*x1+1*x2)+0.8*(2*x1-1*x2)

+(-1)*(-0.3*x1+0.6*x2)+(-1)*(-1.6*x1+0.8*x2)-1.9

=4.3x1+1.4x2-1.9

e1 e2

U=0, V=0.8η=k(4,4)+k(7,7)-2*k(4,7)=5+5-2*(-5)=20α2_new=0.8+((-1)*(7.1-(-10.9))./20)= -0.1α2_new,clipped=0 α1_new=0

α=[0.8 0 0 0 0 0.3 0 0 0 0]‘b=1-(0.8*1*2+0.3*(-1)*1)=1-1.3= -0.3f(x)=0.8*(1*x1+1*x2)+ (-1)*(-0.3*x1+0.6*x2)-0.3 =1.1x1+0.2x2-0.3

2ed iteration

F(x)-Y=[0 0.2 1.8 0.7 0.9 0 -1.3 -1.3 -0.6 -1.9]’ α=[0.8 0 0 0 0 0.3 0.8 0 0 0]‘ U=0, V=0 η=k(3,3)+k(10,10)-2*k(3,10)=1+8-2*(-2)=13 α2_new=0+((-1)*(1.8-(-1.9))./13)=0.28 α2_new,clipped=0 α1_new=0 α=[0.8 0 0 0 0 0.3 0 0 0 0]‘

e1 e2

Trained by Rosenblatt’s Algorithm

Let α1*y1+α2*y2=R

Case 1: y1=1, y2=1 (α1>=0, α2>=0, α1+α2=R>=0)

α1

α2

R=0

R=C

R=2C

C

C

If C<R<2C,

If 0<R<=C,

],[2 CCRnew

],0[2 Rnew

Case 2: y1=-1, y2=1 (R=-α1+α2)

α1

α2

R=-CR=0

R=C

C

C

If -C<R<0,

If 0=<R<C,

],0[2 CRnew

],[2 CRnew

Case 3: y1=-1, y2=-1 (-α1-α2=R<=0)

α1

α2

R=0

R=-C

R=-2C

C

C

If -2C<R<-C,

If -C<=R<0,

],[2 CCRnew

],0[2 Rnew

Case 2: y1=1, y2=-1 (R=α1-α2)

α1

α2

R=CR=0

R=-C

C

C

If 0<=R<C,

If -C<R<0,

],0[2 RCnew

],[2 CRnew



Topic 7 Support Vector Machine for Classification.

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