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A logistic regression approach to distributed IR
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Tampere, Finland
POSTER SESSION: Poster session table of contents
Pages: 399 - 400  
Year of Publication: 2002
ISBN:1-58113-561-0
Author
Ray R. Larson  University of California, Berkeley, Berkeley, California
Sponsor
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 2,   Downloads (12 Months): 39,   Citation Count: 1
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ABSTRACT

This poster session examines a probabilistic approach to distributed information retrieval using a Logistic Regression algorithm for estimation of collection relevance. The algorithm is compared to other methods for distributed search using test collections developed for distributed search evaluation.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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J. Callan. Distributed information retrieval. In W. B. Croft, editor, Advances in Information Retrieval: Recent research from the Center for Intelligent Information Retrieval, chapter 5, pages 127--150. Kluwer, Boston, 2000.
 
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W. S. Cooper, F. C. Gey, and A. Chen. Full text retrieval based on a probabilistic equation with coefficients fitted by logistic regression. In D. K. Harman, editor, The Second Text Retrieval Conference (TREC-2), pages 57--66, Gaithersburg, MD, 1994. NIST.
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