VC-dimension is an index of the capacity of a learning machine. It has been computed in several cases, but always in a Euclidean context. This paper extends the notion to classifiers acting in the more general environment of a manifold. General properties are proved, and some examples of simple classifiers on elementary manifolds are given. A large part of the research is directed towards a still open problem on product manifolds
this paper we present a general scheme for extending the VC-dimension to the case n ? 1. Our scheme ...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
The Vapnik-Chervonenkis dimension VC-dimension(N) of a neural net N with n input nodes is defined as...
VC-dimension is an index of the capacity of a learning machine. It has been computed in several cas...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
We examine the relationship between the VC-dimension and the number of parameters of a smoothly para...
International audienceThe VC-dimension of a set system is a way to capture its complexity and has be...
AbstractN. Linialet al.raised the question of how difficult the computation of the Vapnik–Červonenki...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
Abstract. Inmanymachine learningproblems,high-dimensionaldatasets often lie on or near manifolds of ...
This is the final project report for CPS2341. In this paper, we study several re-cently developed ma...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
AbstractGiven a set F of classifiers and a probability distribution over their domain, one can defin...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
this paper we present a general scheme for extending the VC-dimension to the case n ? 1. Our scheme ...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
The Vapnik-Chervonenkis dimension VC-dimension(N) of a neural net N with n input nodes is defined as...
VC-dimension is an index of the capacity of a learning machine. It has been computed in several cas...
The subject at hand is the dimensionality reduction of statistical manifolds by the use of informati...
We examine the relationship between the VC-dimension and the number of parameters of a smoothly para...
International audienceThe VC-dimension of a set system is a way to capture its complexity and has be...
AbstractN. Linialet al.raised the question of how difficult the computation of the Vapnik–Červonenki...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
Manifold learning has become a vital tool in data driven methods for interpretation of video, motion...
Abstract. Inmanymachine learningproblems,high-dimensionaldatasets often lie on or near manifolds of ...
This is the final project report for CPS2341. In this paper, we study several re-cently developed ma...
Abstract—This paper proposes a new approach to analyze high-dimensional data set using low-dimension...
AbstractGiven a set F of classifiers and a probability distribution over their domain, one can defin...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
this paper we present a general scheme for extending the VC-dimension to the case n ? 1. Our scheme ...
Manifold learning has gained in recent years a great attention in facing the problem of dimensionali...
The Vapnik-Chervonenkis dimension VC-dimension(N) of a neural net N with n input nodes is defined as...
DOI
Add to ORKG