Elements of information theory
Elements of information theory
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Estimation of entropy and mutual information
Neural Computation
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
Learning with matrix factorizations
Learning with matrix factorizations
COLT'07 Proceedings of the 20th annual conference on Learning theory
Prediction by categorical features: generalization properties and application to feature ranking
COLT'07 Proceedings of the 20th annual conference on Learning theory
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
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We derive a generalization bound for multi-classification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space in the form of a Cartesian product of partitions for each of the parameters. The derived bound provides a means to evaluate clustering solutions in terms of the generalization power of a built-on classifier. For classification based on a single feature the bound serves to find a globally optimal classification rule. Comparison of the generalization power of individual features can then be used for feature ranking. Our experiments show that in this role the bound is much more precise than mutual information or normalized correlation indices.