Sensitive error correcting output codes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3559 LNAI, Issue , 2005, Pages 158-172

  Langford, John ^a   Beygelzimer, Alina ^b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); ERROR CORRECTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; ONLINE SYSTEMS; OPTIMIZATION; PROBLEM SOLVING;

BINARY CLASSIFICATION; COST ESTIMATION; COST-SENSITIVE CLASSIFICATION; SENSITIVE ERROR CORRECTION;

CODES (SYMBOLS);

EID: 26944464210     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_11     Document Type: Conference Paper

References (16)

1. Erin Allwein, Robert Schapire, and Yoram Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1:113-141, 2000.
2. Alina Beygelzimer, Varsha Dani, Tom Hayes, and John Langford. Reductions between classification tasks. Electronic Colloquium on Computational Complexity, TR04-077, 2004.
3. Thomas Dietterich and Ghulum Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
4. Yoav Freund and Robert Schapire. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139, 1997.
5. Venkat Guruswami and Amit Sahai. Multiclass learning, boosting, and error-correcting codes. In Proceedings of the 12th Annual Conference on Computational Learning Theory (COLT), 145-155, 1999.
6. Adam Kalai and Rocco Servedio. Boosting in the Presence of Noise. In Proceedings of the 35th Annual ACM Symposium on the Theory of Computing (STOC), 195-205, 2003.
7. John Langford and Bianca Zadrozny. Estimating class membership probabilities using classifier learners. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 2005.
8. Yoonkyung Lee, Yi Lin, and Grace Wahba. Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satelite Radiance Data, Journal of the American Statistical Association, 99, 465 (2004) 67-81.
9. Nick Littlestone and Manfred Warmuth, The Weighted Majority Algorithm, Foundations of Computer Science, 1992.
10. John Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, 61-74, 1999.
11. C. Blake and C. Merz, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine.
12. Leslie Valiant. Learning disjunctions of conjunctions, In Proceedings of the 9th IJCAI, 560-566, 1985.
13. Vladimir Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.
14. Vladimir Vapnik and Alexey Chervonenkis. On the uniform convergence of relative frequencies of event to their probabilities. Theory of Probability and its Applications, 16(2), 264-280, 1971.
15. Ian H. Witten and Eibe Frank, Data Mining: Practical machine learning tools with Java implementations, Morgan Kaufmann, 2000, http://www.cs.waikato. ac.nz/ml/weka/.
16. Bianca Zadrozny, John Langford, and Naoki Abe. Cost-Sensitive Learning by Cost-Proportionate Example Weighting. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM) 435-442, 2003.