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Leave One Out SVM by Weston et al.
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A=LOOM(H) returns a loom object initialized with hyperparameters H.
Hyperparameters, and their defaults
R=1 -- the soft margin parameter K=K-R*diag(diag(K))
A=0.001; -- additional regularization on alphas, A*||alpha||_1
ridge=1e-12; -- a ridge on the kernel
balanced_ridge=0 -- for unbalanced data
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
c1=[2,0];
c2=[-2,0];
X1=randn(50,2)+repmat(c1,50,1);
X2=randn(50,2)+repmat(c2,50,1);
d=data([X1;X2],[ones(50,1);-ones(50,1)]);
[r,a]=train(cv(loom,'folds=2'),d);
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Reference : Leave-One-Out Support Vector Machines. |