PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

ShareBoost: Efficient multiclass learning with feature sharing
Shai Shalev-Shwartz, Yonatan Wexler and Amnon Shashua
In: NIPS 2011, Dec 2011, Granada, Spain.

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 ShareBoost 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 ShareBoost and evaluate its success relatively to other state-of-the-art approaches.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:8910
Deposited By:Shai Shalev-Shwartz
Deposited On:21 February 2012