AbstractThis paper concerns learning binary-valued functions defined on R, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generalization error bounds. We derive error bounds that depend on the sample width (a notion analogous to that of sample margin for real-valued functions). This motivates learning algorithms that seek to maximize sample width
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
AbstractWe present a new general-purpose algorithm for learning classes of [0, 1]-valued functions i...
AbstractWe present a new general upper bound on the number of examples required to estimate all of t...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
In a recent paper [M. Anthony, J. Ratsaby, Maximal width learning of binary functions, Theoretical C...
In M. Anthony and J. Ratsaby. Maximal width learning of binary functions. Theoretical Computer Scien...
In a recent paper, the authors introduced the notion of sample width for binary classifiers defined ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
In this paper we propose a general framework to study the generalization properties of binary classi...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
In this work, the probability of an event under some joint distribution is bounded by measuring it w...
Anumber of results have bounded generalization of a classi er in terms of its margin on the training...
International audienceIn this paper we propose a general framework to study the generalization prope...
A number of results have bounded generalization of a classier in terms of its margin on the training...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
AbstractWe present a new general-purpose algorithm for learning classes of [0, 1]-valued functions i...
AbstractWe present a new general upper bound on the number of examples required to estimate all of t...
This paper concerns learning binary-valued functions defined on, and investigates how a particular t...
In a recent paper [M. Anthony, J. Ratsaby, Maximal width learning of binary functions, Theoretical C...
In M. Anthony and J. Ratsaby. Maximal width learning of binary functions. Theoretical Computer Scien...
In a recent paper, the authors introduced the notion of sample width for binary classifiers defined ...
A number of results have bounded generalization of a classier in terms of its margin on the training...
In this paper we propose a general framework to study the generalization properties of binary classi...
The majority of results in computational learning theory are concerned with concept learning, i.e. w...
In this work, the probability of an event under some joint distribution is bounded by measuring it w...
Anumber of results have bounded generalization of a classi er in terms of its margin on the training...
International audienceIn this paper we propose a general framework to study the generalization prope...
A number of results have bounded generalization of a classier in terms of its margin on the training...
A number of results have bounded generalization error of a classifier in terms of its margin on the ...
We derive new margin-based inequalities for the probability of error of classifiers. The main featur...
AbstractWe present a new general-purpose algorithm for learning classes of [0, 1]-valued functions i...
AbstractWe present a new general upper bound on the number of examples required to estimate all of t...
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