Generalization Bounds via Eigenvalues of the Gram matrix
Model selection in Support Vector machines is usually carried out by min imizing the quotient of the radius of the smallest enclosing sphere of the data and the observed margin on the training set We provide a new crite rion taking the distribution within that sphere into account by considering the Gram matrix of the data In particular this makes use of the eigen value distribution of the matrix Experimental results on real world data show that this new criterion provides a good prediction of the shape of the curve relating generalization error to kernel width