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Volume Regularization for Binary Classification
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BibTeX
@MISC{Crammer_volumeregularization,
author = {Koby Crammer and Tal Wagner},
title = {Volume Regularization for Binary Classification},
year = {}
}
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Abstract
We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains “simple ” weight vectors which most of are accurate on the training set. Two versions of the learning process are cast as convex optimization problems, and it is shown how to solve them efficiently. The formulation yields a natural PAC-Bayesian performance bound and it is shown to minimize a quantity directly aligned with it. The algorithm outperforms SVM and the recently proposed AROW algorithm on a majority of 30 NLP datasets and binarized USPS optical character recognition datasets. 1
Keyphrases
volume regularization binary classification algorithm seek nlp datasets simple weight vector weight vector axis-aligned box natural pac-bayesian performance bound usps optical character recognition datasets convex optimization problem large volume binary prediction large-volume box classification learning process
