- 1.B.T. Bartell, G.W. Cottrell, and R.K. Belew. Automatic combination of multiple ranked retrieval systems. In Proceedings of the 17th Annual International Conference on Research and Development in Information Retrieval, 1994. Google ScholarDigital Library
- 2.Chris Buckley, James Allan, and Gerard Salton. Automatic routing and ad-hoc retrieval using SMART: TREC 2. In Proceedings of the Second Text Retrieval Conference, pages 45-56, 1994. Google ScholarDigital Library
- 3.Chris Buckley and Gerard Salton. Optimization of relevance feedback weights. In Proceedings of the 18th Annual International Conference on Research and Development in Information Retrieval, pages 351- 357, July 1995. Google ScholarDigital Library
- 4.William W. Cohen, Robert E. Schapire, and Yoram Singer. Learning to order things. Journal of Artificial Intelligence Research, 10:243- 270, 1999. Google ScholarCross Ref
- 5.Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer. An efficient boosting algorithm for combining preferences. In Machine Learning: Proceedings of the Fifteenth International Conference, 1998. Google ScholarDigital Library
- 6.Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997. Google ScholarDigital Library
- 7.Warren R. Greiff. A theory of term weighting based on exploratory data analysis. In Proceedings of the 21st International Conference on Research and Development in Information Retrieval, pages 11-19, 1998. Google ScholarDigital Library
- 8.Donna Harman. Overview of the third text retrieval conference. In Proceedings of the Third Text Retrieval Conference, pages 1-27, 1995.Google Scholar
- 9.S. E. Robertson. The probability ranking principle in IR. Journal of Documentation, 33(4):294-304, December 1977.Google ScholarCross Ref
- 10.S. E. Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, and M. Gatford. Okapi at TREC-3. In Proceedings of the Third Text Retrieval Conference, pages 109-126, 1995.Google Scholar
- 11.J. Rocchio. Relevance feedback information retrieval. In The Smart retrieval system-experiments in automatic document processing, pages 313-323. Prentice Hall, 1971.Google Scholar
- 12.J.J. Rocchio. Document Retrieval Systems-Optimization and Evaluation. PhD thesis, Harvard Computational Laboratory, 1966.Google Scholar
- 13.Gerard Salton and Christopher Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513-523, 1988. Google ScholarDigital Library
- 14.Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, October 1998.Google ScholarCross Ref
- 15.Robert E. Schapire and Yoram Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297- 336, December 1999. Google ScholarDigital Library
- 16.Robert E. Schapire, Yoram Singer, and Amit Singhal. Boosting and Rocchio applied to text filtering. In Proceedings of the 21st Annual International Conference on Research and Development in Information Retrieval, 1998. Google ScholarDigital Library
- 17.Amit Singhal, Chris Buckley, and Mandar Mitra. Pivoted document length normalization. In Proceedings of the 19th Annual International Conference on Research and Development in Information Retrieval, pages 21-29, 1996. Google ScholarDigital Library
- 18.Amit Singhal, Mandar Mitra, and Chris Buckley. Learning routing queries in a query zone. In Proceedings of the 20th Annual International Conference on Research and Development in Information Retrieval, pages 25-32, 1997. Google ScholarDigital Library
- 19.Howard Turtle and W. Bruce Croft. Inference networks for document retrieval. In Proceedings of the 13th Annual International Conference on Research and Development in Information Retrieval, pages 1-24, 1990. Google ScholarDigital Library
- 20.C. J. van Rijsbergen. Information Retrieval. Butterworths, London, second edition, 1979. Google ScholarDigital Library
- 21.E.M. Voorhees and D.K. Harman. Overview of the sixth text retrieval conference. In Proceedings of the Sixth Text Retrieval Conference, pages 1-24, 1998.Google ScholarCross Ref
Index Terms
- Boosting for document routing
Recommendations
A multi-class boosting method with direct optimization
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningWe present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class ...
Boosting recombined weak classifiers
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting ...
Using boosting to prune bagging ensembles
Boosting is used to determine the order in which classifiers are aggregated in a bagging ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging ensemble allows the identification of subensembles that require less memory ...
Comments