AbstractWe define embeddings between concept classes that are meant to reflect certain aspects of their combinatorial structure. Furthermore, we introduce a notion of universal concept classes - classes into which any member of a given family of classes can be embedded. These universal classes play a role similar to that played in computational complexity by languages that are hard for a given complexity class. We show that classes of half-spaces in Rn are universal with respect to families of algebraically defined classes.We present some combinatorial parameters along which the family of classes of a given VC-dimension can be grouped into sub-families. We use these parameters to investigate the existence of embeddings and the scope of univ...
We show that the topes of a complex of oriented matroids (abbreviated COM) of VC-dimension $d$ admit...
One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-a...
One of the open problems in machine learning is whether any set-family of VC-dimension d admits a s...
AbstractWe define embeddings between concept classes that are meant to reflect certain aspects of th...
Abstract. Within the framework of pac-learning, we explore the learnability of concepts from samples...
Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine learnin...
Within the framework of pac-learning, we explore the learnability of concepts from samples using the...
This paper presents a construction of a proper and stable labelled sample compression scheme of size...
. Within the framework of pac-learning, we explore the learnability of concepts from samples using t...
Abstract. Sample compression schemes are schemes for “encoding ” a set of examples in a small subset...
Any set of labeled examples consistent with some hidden orthogonal rectan-gle can be \compressed &qu...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Maximum concept classes of VC dimension d over n domain points have size � n � ≤d, and this is an up...
Any set of labeled examples consistent with some hidden orthogonal rectan-gle can be “compressed ” t...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
We show that the topes of a complex of oriented matroids (abbreviated COM) of VC-dimension $d$ admit...
One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-a...
One of the open problems in machine learning is whether any set-family of VC-dimension d admits a s...
AbstractWe define embeddings between concept classes that are meant to reflect certain aspects of th...
Abstract. Within the framework of pac-learning, we explore the learnability of concepts from samples...
Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine learnin...
Within the framework of pac-learning, we explore the learnability of concepts from samples using the...
This paper presents a construction of a proper and stable labelled sample compression scheme of size...
. Within the framework of pac-learning, we explore the learnability of concepts from samples using t...
Abstract. Sample compression schemes are schemes for “encoding ” a set of examples in a small subset...
Any set of labeled examples consistent with some hidden orthogonal rectan-gle can be \compressed &qu...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Maximum concept classes of VC dimension d over n domain points have size � n � ≤d, and this is an up...
Any set of labeled examples consistent with some hidden orthogonal rectan-gle can be “compressed ” t...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
We show that the topes of a complex of oriented matroids (abbreviated COM) of VC-dimension $d$ admit...
One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-a...
One of the open problems in machine learning is whether any set-family of VC-dimension d admits a s...