Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
More theorems about scale-sensitive dimensions and learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
On the size of convex hulls of small sets
The Journal of Machine Learning Research
On the size of convex hulls of small sets
The Journal of Machine Learning Research
Wavelet-RKHS-based functional statistical classification
Advances in Data Analysis and Classification
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We explore the question of the learnability of classes of functions contained in a Hilbert space which has a reproducing kernel. We show that if the evaluation functionals are uniformly bounded and if the class is norm bounded then it is learnable. We formulate a learning procedure related to the well known support vector machine (SVM), which requires solving a system of linear equations, rather than the quadratic programming needed for the SVM. As a part of our discussion, we estimate the fat-shattering dimension of the unit ball of the dual of a Banach space when considered as a set of functions on the unit ball of the space itself. Our estimate is based on a geometric property of the Banach space called the type.