Abstract

Systems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers. We propose that two machine learning algorithms, the Widrow-Hoff and EG algorithms, be used in training linear text classifiers. In contrast to most IR methods, theoretical analysis provides performance guarantees and guidance on parameter settings for these algorithms. Experimental data is presented showing Widrow-Hoff and EG to be more effective than the widely used Rocchio algorithm on several categorization and routing tasks. 1 Introduction Document retrieval, categorization, routing, and filtering systems often are based on classification. That is, the IR system decides for each document which of two or more classes it belongs to, or how strongly it belongs to a class, in order to accomplish the IR task of interest. For instance, the two classes may be the documents relevant to and not relevant to a particular user, and the system may rank documents based on how likely it i...

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