Unlabeled compression schemes for maximum classes

Authors:
Dima Kuzmin;Manfred K. Warmuth
Affiliations:
Computer Science Department, University of California, Santa Cruz;Computer Science Department, University of California, Santa Cruz
Venue:
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Year:
2005

Citing 9
Cited 1

Predicting {0, 1}-functions on randomly drawn points

Information and Computation
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

Machine Learning
Linear Algebraic Proofs of VC-Dimension Based Inequalities

EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
On space-bounded learning and the vapnik-chervonenkis dimension

On space-bounded learning and the vapnik-chervonenkis dimension
The set covering machine

The Journal of Machine Learning Research
A Compression Approach to Support Vector Model Selection

The Journal of Machine Learning Research
Tutorial on Practical Prediction Theory for Classification

The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
The one-inclusion graph algorithm is near-optimal for the prediction model of learning

IEEE Transactions on Information Theory

Shifting: One-inclusion mistake bounds and sample compression

Journal of Computer and System Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 of the sample.