BibTeX
@MISC{_rs,
author = {},
title = {R S},
year = {}
}
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Abstract
Discriminative models are a class of learning methods where the focus is on learning class memberships, as opposed to Generative models, where the interest is in full class densities. While several approaches to discriminative modelling exist, we concentrate on the Maximum Entropy Framework, based on a theoretical argument developed by Jaynes [1957]. Maximum Entropy methods are featurebased: in order to infer an empirical distribution from the data they encode relevant statistics using features. In general, the quality of the model grows with the number and scope of features: unfortunately, the computational and memory resources needed to manipulate them also grow accordingly, often to an unmanageable extent. We investigate the possibility of representing features using randomised techniques. Exploring one class of important one-sided error randomised data structures derived from the Bloom Filter, our study concentrates on the logarithmicfrequency Bloom Filter [Talbot and Osborne, 2007a,b] and the Bloom Map [Talbot and Talbot, 2008]. Both are introduced and tested in a discriminative learning
