  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.
