ACM Home Page
Please provide us with feedback. Feedback
Inductive learning algorithms and representations for text categorization
Full text PdfPdf (1.34 MB)
Source Conference on Information and Knowledge Management archive
Proceedings of the seventh international conference on Information and knowledge management table of contents
Bethesda, Maryland, United States
Pages: 148 - 155  
Year of Publication: 1998
ISBN:1-58113-061-9
Authors
Susan Dumais  Microsoft Research, One Microsoft way, Redmond, WA
John Platt  Microsoft Research, One Microsoft way, Redmond, WA
David Heckerman  Microsoft Research, One Microsoft way, Redmond, WA
Mehran Sahami  Computer Science Department, Standford University, Standford, CA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
SIGMIS: ACM Special Interest Group on Management Information Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 37,   Downloads (12 Months): 319,   Citation Count: 171
Additional Information:

references   cited by   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Review this Article  
Save this Article to a Binder    Display Formats: BibTex  EndNote ACM Ref   
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/288627.288651
What is a DOI?

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.

1
 
2
Apte, C., Damerau, F. and Weiss, S.. Text Mining with decision rules and decision trees. Proceedings of the Conference on Automated Learning and Discovery, CMU, June, 1998.
3
 
4
Chickering D., Heckerman D., and Meek, C. A Bayesian approach for learning Bayesian networks with local structure. In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence, 1997.
5
 
6
 
7
Fuhr, N., Hartmanna, S., Lustig, G., Schwantner, M., and Tzeras, K. Air/X- A rule-based multi-stage indexing system for lage subject fields. In Proceedings of RIAO'91, 606-623, 1991.
 
8
Good, I.J. The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, 1965.
 
9
 
10
 
11
 
12
LeCun, Y., Jackel, L. D., Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, i., Muller, U. A., Sackinger, E., Simard, P. and Vapnik, V. Learning algorithms for classification: A comparison on handwritten digit recognition. Neural Networks: The Statistical Mechanics Perspective, 261-276, 1995.
13
 
14
Lewis, D.D. and Hayes, P.J. (Eds.)ACM Transactions on Information Systems- Special Issue on Text Categorization, 12(3), 1994.
 
15
Lewis, D.D. and Ringuette, M.. A comparison of two learning algorithms for text categorization. In Third Annual Symposium on Document Analysis and Information Retrieval, 81-93, 1994.
16
17
 
18
 
19
 
20
Rocchio, J.J. Jr. Relevance feedback in information retrieval, in G.Salton (Ed.), The SMART Retrieval System: Experiments in Automatic Document Processing, 313-323. Prentice Hall, 1971.
 
21
Sahami, M. Learning Limited Dependence Bayesian Classifiers. In KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 335-338, AAAI Press, 1996. httr>://robotics.stanford.edu/users/sahami/papersdir/kdd96-1earn-bn.ps
 
22
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E. A Bayesian approach to filtering junk e-mail. AAAI 98 Workshop on Text Categorization, July 1998. http://robotics.stanford.edu/users/sahami/papersdir/spam.ps
 
23
 
24
Schapire, R., Freund, Y., Bartlett, P. and Lee, W. S. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, to appear, 1998.
25
 
26
 
27
Wiener E., Pedersen, J.O. and Weigend, A.S. A neural network approach to topic spotting. In Proceedings of the Fourth Annual Symposium on Document Analysis and Information Retrieval (SDAIR'95), 1995.
 
28
29
 
30
 
31
Yang, Y. An evaluation of statistical approaches to text categorization. CMU Technical Report, CMU-CS- 97-127, April 1997.
 
32
The Reuters-21578 collection is available at: http://www.research.att.conff-lewis/reuters2157 8.html

CITED BY  171