Using transduction and multi-view learning to answer emails

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2838, Issue , 2003, Pages 266-277

  Kockelkorn, Michael ^a   Lüneburg, Andreas ^a   Scheffer, Tobias ^b  

^a Tonxx   (Germany)

^b HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; COMMUNICATION SYSTEMS; ELECTRONIC MAIL; PROBLEM SOLVING; SOCIETIES AND INSTITUTIONS; DATABASE SYSTEMS; SUPERVISED LEARNING;

CUSTOMER SERVICE; EMAIL ANSWERING; MULTI-VIEW LEARNING; SOFTWARE SUPPORT; CORPORATE CUSTOMERS; FREQUENTLY ASKED QUESTIONS; LARGE AMOUNTS; SEMI-SUPERVISED CLASSIFICATION; TRANSDUCTIVE SUPPORT VECTOR MACHINE;

LEARNING SYSTEMS; ELECTRONIC MAIL;

EID: 9444281494     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39804-2_25     Document Type: Conference Paper

References (20)

1. I. Androutsopoulos, J. Koutsias, K. Chandrinos, and C. Spyropoulos. An experimental comparison of naive bayesian and keaword based anti-spam filtering with personal email messsages. In Proceedings of the International ACM SIGIR Conference, 2000.
2. P. Bennett. Assessing the calibration of naive bayes' posterior estimates. Technical report, CMU, 2000.
3. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the Workshop on Computational Learning Theory, 1998.
4. T. Boone. Concept features in Re:Agent, an intelligent email agent. Autonomous Agents, 1998.
5. A. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145-1159, 1997.
6. W. Cohen. Learnig rules that classify email. In Proceedings of the IEEE Spring Symposium on Machine learning for Information Access, 1996.
7. E. Crawford, J. Kay, and E. McCreath. IEMS - the intelligent email sorter. In Proceedings of the International Conference on Machine Learning, 2002.
8. C. Green and P. Edwards. Using machine learning to enhance software tools for internet information management. In Proceedings of the AAAI Workshop on Internet Information Management, 1996.
9. T. Joachims. Making large-scale svm learning practical. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1999.
10. T. Joachims. Transductive inference for text classification using support vector machines. In Proceedings of the International Conference on Machine Learning, 1999.
11. S. Kiritchenko and S. Matwin. Email classification with co-training. Technical report, University of Ottawa, 2002.
12. A. Kolcz and J. Alspector. Svm-based filtering of e-mail spam with content-specific misclassification costs. In Proceedings of the ICDM Workshop on Text Mining, 2001.
13. D. Lewis. The trec-5 filtering track. In Proceedings of the Fifth Text Retrieval Conference, 1997.
14. I. Muslea, C. Kloblock, and S. Minton. Active + semi-supervised learning = robust multi-view learning. In Proceedings of the International Conference on Machine Learning, 2002.
15. K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proceedings of Information and Knowledge Management, 2000.
16. P. Pantel and D. Lin. Spamcop: a spam classification and organization program. In Proceedings of the AAAI Workshop on Learning for Text Categorization, 1998.
17. F. Provost, T. Fawcett, and R. Kohavi. The case against accuracy estimation in comparing classifiers. In Proceedings of the International Conference on Machine Learning, 1998.
18. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A bayesian approach to filtering junk email. In Proceedings of the AAAI Workshop on Learning for Text Categorization, 1998.
19. R. Segal and J. Kephart. Mailcat: An intelligent assistant for organizing mail. In Autonomous Agents, 1999.
20. E. Vorhees. The trec-8 question answering track report. In Proceedings of TREC-8, 1999.