Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Artificial Intelligence - Special issue on relevance
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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This paper addresses the problem of text classification in high dimensionality spaces by applying linear weight updating classifiers that have been highly studied in the domain of machine learning. Our experimental results are based on the Winnow family of algorithms that are simple to implement and efficient in terms of computation time and storage requirements. We applied an exponential multiplication function to weight updates and we experimentally calculated the optimal values of the learning rate and the separating surface parameters. Our results are at the level of the best results that were reported on the family of linear algorithms and perform nearly as well as the top performing methodologies in the literature.