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Generalization error bounds for the logical analysis of data

By Martin Anthony


This paper analyzes the predictive performance of standard techniques for the 'logical analysis of data' (LAD), within a probabilistic framework. We bound the generalization error of classifiers produced by standard LAD methods in terms of their complexity and how well they fit the training data. We also quantify the predictive accuracy in terms of the extent to which the underlying LAD discriminant function achieves a large separation (a 'large margin') between (most of) the positive and negative observation

Topics: QA Mathematics
Publisher: Elsevier
Year: 2012
DOI identifier: 10.1016/j.dam.2011.12.001
OAI identifier:
Provided by: LSE Research Online
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