Exploring the similarity space
ACM SIGIR Forum
Making large-scale support vector machine learning practical
Advances in kernel methods
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
MP-Boost: a multiple-pivot boosting algorithm and its application to text categorization
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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We propose a method by means of which supervised learning algorithms that only accept binary input can be extended to use ordinal (i.e., integer-valued) input. This is much needed in text classification, since it becomes thus possible to endow these learning devices with term frequency information, rather than just information on the presence/absence of the term in the document. We test two different learners based on "boosting", and show that the use of our method allows them to obtain effectiveness gains. We also show that one of these boosting methods, once endowed with the representations generated by our method, outperforms an SVM learner with tfidf-weighted input.