Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension
Journal of Combinatorial Theory Series A
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Approximation and learning of convex superpositions
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Efficient distribution-free learning of probabilistic concepts
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Scale-sensitive dimensions, uniform convergence, and learnability
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
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This paper presents new results about the confidence bounds on the generalization performances of perceptrons. It deals with regression problems. It is shown that the probability to get a generalization error greater than the empirical error plus a precision e, depends on the number of inputs and on the magnitude of the coefficients of the combination. The result presented does not require to bound a priori the magnitude of these coefficients, the size and the number of layers.