Efficient Multiclass Implementations of L1-Regularized Maximum Entropy

Details Efficient Multiclass Implementations of L1-Regularized Maximum Entropy Efficient Multiclass Implementations of L1-Regularized Maximum Entropy

2005-06-29

arxiv.org/abs/cs/0506101v1

Computer Science

Learning

13 pages, describes new conditional maxent algorithm, to be submitted

Scientific paper

This paper discusses the application of L1-regularized maximum entropy modeling or SL1-Max [9] to multiclass categorization problems. A new modification to the SL1-Max fast sequential learning algorithm is proposed to handle conditional distributions. Furthermore, unlike most previous studies, the present research goes beyond a single type of conditional distribution. It describes and compares a variety of modeling assumptions about the class distribution (independent or exclusive) and various types of joint or conditional distributions. It results in a new methodology for combining binary regularized classifiers to achieve multiclass categorization. In this context, Maximum Entropy can be considered as a generic and efficient regularized classification tool that matches or outperforms the state-of-the art represented by AdaBoost and SVMs.

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