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Multi-class Support Vector Machine by J.Weston
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A=MC_SVM(H) returns an mc_svm object initialized with hyperparameters H.
Multi-class Support Vector Machine, solving a single
optimization problem.
Hyperparameters, and their defaults
C=Inf -- the soft margin C parameter
child=kernel -- the kernel is stored as a member called "child"
Model
alpha -- the weights
b0 -- the threshold
Xsv -- the Support Vectors
Methods:
train, test, get_w
Example:
c1=[-1,1];c2=[1,1];c3=[0,-1];
X1= randn(50,2)+repmat(c1,50,1);
X2= randn(50,2)+repmat(c2,50,1);
X3= randn(50,2)+repmat(c3,50,1);
note the class label format!
Y1= [ones(50,1),-ones(50,1),-ones(50,1)];
Y2= [-ones(50,1),ones(50,1),-ones(50,1)];
Y3= [-ones(50,1),-ones(50,1),ones(50,1)];
d=data([X1;X2;X3],[Y1;Y2;Y3]);
[r,a]=train(mc_svm(kernel('rbf',2)),d)
Test class centers
dtest=data([c1;c2;c3]);
rtest=test(a,dtest)
plot(a);
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Reference : Multi-class Support Vector Machines |
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Author : Jason Weston, Chris Watkins |