@inproceedings{484c0e156823433faa9a63bd001f014b,
title = "ShareBoost: Efficient multiclass learning with feature sharing",
abstract = "Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sublinearly with the number of possible classes. This implies that features should be shared by several classes. We describe and analyze the ShareBoost algorithm for learning a multiclass predictor that uses few shared features. We prove that Share-Boost efficiently finds a predictor that uses few shared features (if such a predictor exists) and that it has a small generalization error. We also describe how to use ShareBoost for learning a non-linear predictor that has a fast evaluation time. In a series of experiments with natural data sets we demonstrate the benefits of Share-Boost and evaluate its success relatively to other state-of-the-art approaches.",
author = "Shai Shalev-Shwartz and Yonatan Wexler and Amnon Shashua",
year = "2011",
language = "American English",
isbn = "9781618395993",
series = "Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011",
booktitle = "Advances in Neural Information Processing Systems 24",
note = "25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 ; Conference date: 12-12-2011 Through 14-12-2011",
}