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BOOSTEXTER A BOOSTING-BASED SYSTEM FOR TEXT CATEGORIZATION PDF

We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the. BoosTexter is a general purpose machine-learning program based on boosting for building a BoosTexter: A boosting-based system for text categorization. BoosTexter: A Boosting-based Systemfor Text Categorization . In Advances in Neural Information Processing Systems 8 (pp. ). 8.

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The boosting approach to machine learning: A brief introduction to boosting RE Schapire Ijcai 99, Improved boosting algorithms using confidence-rated predictions RE Schapire, Y Singer Machine learning 37 3, Nonlinear estimation and classification, Large margin classification using the perceptron algorithm Y Freund, RE Schapire Machine learning 37 3, Advances in Neural Information Processing Systems, A decision-theoretic generalization of on-line learning and an application to boosting Y Freund, RE Schapire Journal of computer and system sciences 55 1, See our FAQ for additional information.

Advances in neural information processing systems, References Publications referenced by this paper.

BoosTexter: A Boosting-based System for Text Categorization

Categorization Search for additional papers on this topic. New citations to this author. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We describe in detail an implementation, called Blostexter, of the new boosting algorithms for text categorization tasks.

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BoosTexter: A Boosting-based System for Text Categorization

McCarthyDanielle S. An overview RE Schapire Nonlinear estimation and classification, Automaticacquisition of salient grammar fragments for call – type classification.

The following articles are merged in Scholar. Ecography 29 2, Their combined citations are counted only for the boosging-based article. Showing of 1, extracted citations. This paper has 2, citations. This paper has highly influenced other papers.

Robert Schapire – Google Scholar Citations

Proceedings of the 19th international conference on World wide web, Arcing Classifiers Leo Breiman Semantic Scholar estimates that this publication has 2, citations based on the available data. Our approach is based on a new and improved family of boosting algorithms.

An evaluation of statistical approaches. This “Cited by” count includes citations to the following articles in Scholar.

BoosTexter

Journal of computer and system sciences 55 1, Topics Discussed in This Paper. Articles 1—20 Show foe. Email address for updates. Citations Publications citing this paper. Proceedings of the twenty-first international conference on Machine learning, systme Reducing multiclass to binary: The strength of weak learnability RE Schapire Machine learning 5 2, An evaluation of statistical approaches to text categorization.

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My profile My library Metrics Alerts. Systeem system can’t perform the operation now. Get my own profile Cited by View all All Since Citations h-index 75 54 iindex Journal of machine learning research 1 Dec, Categorization Boosting machine learning.

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