AbstractWe present a new general-purpose algorithm for learning classes of [0, 1]-valued functions in a generalization of the prediction model and prove a general upper bound on the expected absolute error of this algorithm in terms of a scale-sensitive generalization of the Vapnik dimension proposed by Alon, Ben-David, Cesa-Bianchi, and Haussler. We give lower bounds implying that our upper bounds cannot be improved by more than a constant factor in general. We apply this result, together with techniques due to Haussler and to Benedek and Itai, to obtain new upper bounds on packing numbers in terms of this scale-sensitive notion of dimension. Using a different technique, we obtain new bounds on packing numbers in terms of Kearns and Schapi...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
AbstractWe show that the class FBV of [0,1]-valued functions with total variation at most 1 can be a...
We present a new general-purpose algorithm for learning classes of [0, 1]-valued functions in a gene...
Learnability in Valiant's PAC learning model has been shown to be strongly related to the exist...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
Abstract. The Vapnik-Chervonenkis (VC) dimension plays an important role in statistical learning the...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe investigate the PAC learnability of classes of {0, ..., n}-valued functions (n < ∞). For ...
AbstractWe consider the problem of learning real-valued functions from random examples when the func...
AbstractWe investigate the PAC learnability of classes of {0, ..., n}-valued functions (n < ∞). For ...
AbstractGiven a set F of classifiers and a probability distribution over their domain, one can defin...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
this paper we present a general scheme for extending the VC-dimension to the case n ? 1. Our scheme ...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
AbstractWe show that the class FBV of [0,1]-valued functions with total variation at most 1 can be a...
We present a new general-purpose algorithm for learning classes of [0, 1]-valued functions in a gene...
Learnability in Valiant's PAC learning model has been shown to be strongly related to the exist...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
In this thesis we study the generalized Glivenko-Cantelli theorem and its application in mathematica...
Abstract. The Vapnik-Chervonenkis (VC) dimension plays an important role in statistical learning the...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe investigate the PAC learnability of classes of {0, ..., n}-valued functions (n < ∞). For ...
AbstractWe consider the problem of learning real-valued functions from random examples when the func...
AbstractWe investigate the PAC learnability of classes of {0, ..., n}-valued functions (n < ∞). For ...
AbstractGiven a set F of classifiers and a probability distribution over their domain, one can defin...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
this paper we present a general scheme for extending the VC-dimension to the case n ? 1. Our scheme ...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
AbstractWe show that the class FBV of [0,1]-valued functions with total variation at most 1 can be a...