ShareBoost: Efficient multiclass learning with feature sharing

Shai Shalev-Shwartz*, Yonatan Wexler, Amnon Shashua

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

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.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 24
Subtitle of host publication25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
PublisherNeural Information Processing Systems
ISBN (Print)9781618395993
StatePublished - 2011
Event25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 - Granada, Spain
Duration: 12 Dec 201114 Dec 2011

Publication series

NameAdvances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Conference

Conference25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
Country/TerritorySpain
CityGranada
Period12/12/1114/12/11

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