BOOSTEXTER A BOOSTING-BASED SYSTEM FOR TEXT CATEGORIZATION PDF

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks.

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks.

Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. View on Springer. Save to Library.

Create Alert. Launch Research Feed. Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Categorization Boosting machine learning Document classification Algorithm. Citations Publications citing this paper. McCarthy , Danielle S. Exploiting random projections and sparsity with random forests and gradient boosting methods - Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity Arnaud Joly Mathematics, Computer Science ArXiv References Publications referenced by this paper.

Developments in automatic text retrieval. Maximizing text-mining performance S. Hampp Computer Science Arcing Classifiers Leo Breiman Automatic acquisition of salient grammar fragments for call-type classification Jeremy H. Wright , Allen L. Boosting the margin: A new explanation for the effectiveness of voting methods Robert E. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

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BoosTexter: a boosting-based system for text categorization

Robert E. Schapire , Yoram Singer. This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms.

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BoosTexter: A Boosting-based System for Text Categorization

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BoosTexter

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