Abstract (for dissemination) The Importance of Parameter Tuning in Text Categorization The Importance of Parameter Tuning in Text Categorization
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by Author(s K. Koster
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Abstract:
Number of pages 9 WP contributing to the deliverable
Citations
| 591 | Relevance feedback in information retrieval – Rocchio - 1971 |
| 554 | Machine learning in automated text categorization – Sebastiani - 2002 |
| 202 | Context-sensitive learning methods for text categorization – Cohen, Singer - 1996 |
| 195 | Automated learning of decision rules for text categorization – Apte, Damerau, et al. - 1994 |
| 79 | Mistake-Driven Learning in Text Categorization – Dagan, Karov, et al. - 1997 |
| 69 | General convergence results for linear discriminant updates – Grove, Littlestone, et al. - 1997 |
| 51 | Learning routing queries in a query zone – Singhal, Mitra, et al. - 1997 |
| 42 | A Linguistically Motivated Probabilistic Model of Information Retrieval – Hiemstra - 1998 |
| 35 | A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization – Caropreso, Matwin, et al. - 2001 |
| 30 | A new family of online algorithms for category ranking – Crammer, Singer - 2002 |
| 11 | Uncertainty-based Noise Reduction and Term Selection in Text Categorization – Peters, Koster |
