Sciweavers

Share
DATAMINE
1998
145views more  DATAMINE 1998»
9 years 3 months ago
A Tutorial on Support Vector Machines for Pattern Recognition
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
Christopher J. C. Burges
EUROCOLT
1997
Springer
9 years 8 months ago
Vapnik-Chervonenkis Dimension of Recurrent Neural Networks
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedforward networks. However, recurrent networks are also widely used in learning app...
Pascal Koiran, Eduardo D. Sontag
COLT
2001
Springer
9 years 8 months ago
Limitations of Learning via Embeddings in Euclidean Half-Spaces
The notion of embedding a class of dichotomies in a class of linear half spaces is central to the support vector machines paradigm. We examine the question of determining the mini...
Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon
COCO
2001
Springer
142views Algorithms» more  COCO 2001»
9 years 8 months ago
On the Complexity of Approximating the VC Dimension
We study the complexity of approximating the VC dimension of a collection of sets, when the sets are encoded succinctly by a small circuit. We show that this problem is • Σp 3-...
Elchanan Mossel, Christopher Umans
COLT
2003
Springer
9 years 9 months ago
Learning with Rigorous Support Vector Machines
We examine the so-called rigorous support vector machine (RSVM) approach proposed by Vapnik (1998). The formulation of RSVM is derived by explicitly implementing the structural ris...
Jinbo Bi, Vladimir Vapnik
ILP
2005
Springer
9 years 9 months ago
Generalization Behaviour of Alkemic Decision Trees
Abstract. This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical lea...
Kee Siong Ng
COLT
2005
Springer
9 years 9 months ago
Unlabeled Compression Schemes for Maximum Classes,
We give a compression scheme for any maximum class of VC dimension d that compresses any sample consistent with a concept in the class to at most d unlabeled points from the domain...
Dima Kuzmin, Manfred K. Warmuth
books