Communications of the ACM
What size net gives valid generalization?
Neural Computation
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Neural Computation
A universal theorem on learning curves
Neural Networks
Statistical theory of learning curves under entropic loss criterion
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics)
An asymptotic statistical theory of polynomial kernel methods
Neural Computation
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The generalization properties of learning classifiers with a polynomial kernel function are examined here. We first show that the generalization error of the learning machine depends on the properties of the separating curve, that is, the intersection of the input surface and the true separating hyperplane in the feature space. When the input space is one-dimensional, the problem is decomposed to as many one-dimensional problems as the number of the intersecting points. Otherwise, the generalization error is determined by the class of the separating curve. Next, we consider how the class of the separating curve depends on the true separating function. The class is maximum when the true separating polynomial function is irreducible and smaller otherwise. In either case, the class depends only on the true function and does not on the dimension of the feature space. The results imply that the generalization error does not increase even when the dimension of the feature space gets larger and that the so-called overmodeling does not occur in the kernel learning.