Robust cutpoints in the logical analysis of numerical data

Authors:
Martin Anthony;Joel Ratsaby
Affiliations:
Department of Mathematics, The London School of Economics and Political Science, Houghton Street, London WC2A2AE, UK;Electrical and Electronics Engineering Department, Ariel University Center of Samaria, Ariel 40700, Israel
Venue:
Discrete Applied Mathematics
Year:
2012

Citing 10
Cited 2

Learnability and the Vapnik-Chervonenkis dimension

Journal of the ACM (JACM)
Cause-effect relationships and partially defined Boolean functions

Annals of Operations Research
Logical analysis of numerical data

Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Learning in Neural Networks: Theoretical Foundations

Learning in Neural Networks: Theoretical Foundations
Advances in Large Margin Classifiers

Advances in Large Margin Classifiers
An Implementation of Logical Analysis of Data

IEEE Transactions on Knowledge and Data Engineering
Function Learning from Interpolation

Combinatorics, Probability and Computing
Maximal width learning of binary functions

Theoretical Computer Science
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

IEEE Transactions on Information Theory

Analysis of a multi-category classifier

Discrete Applied Mathematics
Learning bounds via sample width for classifiers on finite metric spaces

Theoretical Computer Science

Quantified Score

Hi-index	0.05

Visualization

Abstract

Techniques for the logical analysis of binary data have successfully been applied to non-binary data which has been 'binarized' by means of cutpoints; see Boros et al. (1997, 2000) [7,8]. In this paper, we analyze the predictive performance of such techniques and, in particular, we derive generalization error bounds that depend on how 'robust' the cutpoints are.