Adaptive error-correcting output codes

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1932-1938

  Zhong, Guoqiang ^a   Cheriet, Mohamed ^a  

^a Synchromedia Laboratory   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; EFFECTIVENESS AND EFFICIENCIES; EMPIRICAL STUDIES; ERROR CORRECTING OUTPUT CODE; HANDWRITTEN DIGITS RECOGNITION; MULTI-CLASS LEARNING; MULTITASK LEARNING; UCI MACHINE LEARNING REPOSITORY;

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS;

CLASSIFICATION (OF INFORMATION);

EID: 84896064088     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research, 1:113-141, 2000.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Convex Multi-Task Feature Learning. Machine Learning, 73(3):243-272, 2008.
3. J. Barzilai and J. M. Borwein. Two-point sStep Size Gradient Methods. IMA J. Numer. Anal., 8:141-148, 1988.
4. Y. Bengio, J.-F. Paiement, P. Vincent, O. Delalleau, N. Le Roux, and M. Ouimet. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In NIPS, 2003.
5. L. Breiman. Random Forests. Machine Learning, 45(1):5-32, 2001.
6. O. Dekel and Y. Singer. Multiclass Learning by Probabilistic Embeddings. In NIPS, pages 945-952, 2002.
7. Janez Demsar. Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7:1-30, 2006.
8. T. G. Dietterich and G. Bakiri. Solving Multiclass Learning Problems via Error- Correcting Output Codes. J. Artif. Intell. Res. (JAIR), 2:263-286, 1995.
9. S. Escalera and O. Pujol. ECOCONE: A Novel Coding and Decoding Strategy. In ICPR, pages 578-581, 2006.
10. S. Escalera, O. Pujol, and P. Radeva. On the Decoding Process in Ternary Error-Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1):120-134, 2010.
11. R. A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7:179-188, 1936.
12. M. Friedman. The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32:675-701, 1937.
13. M. Friedman. A Comparison of Alternative Tests of Significance for the Problem of m Rankings. Annals of Mathematical Statistics, 11:86-92, 1940.
14. R. Ghani. Combining Labeled and Unlabeled Data for Text Classification with a Large Number of Categories. In ICDM, pages 597-598, 2001.
15. T. Hastie and R. Tibshirani. Classification by Pairwise Coupling. Annals of Statistics, 26(2):451-471, 1998.
16. X. He and P. Niyogi. Locality Preserving Projections. In NIPS, 2003.
17. I.T. Jolliffe. Principal Component Analysis. New York: Springer-Verlag, 1986.
18. J. Kittler, R. Ghaderi, T. Windeatt, and J. Matas. Face Verification Using Error Correcting Output Codes. In CVPR (1), pages 755-760, 2001.
19. A. Kumar and H. Daumé III. Learning Task Grouping and Overlap in Multi-task Learning. In ICML, 2012.
20. J. Liu, S. Ji, and J. Ye. Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization. In UAI, pages 339-348, 2009.
21. P. B. Nemenyi. Distribution-Free Multiple Comparisons. PhD thesis, Princeton University, 1963.
22. N. J. Nilsson. Learning Machines. McGraw- Hill, 1965.
23. A. Passerini, M. Pontil, and P Frasconi. New Results on Error Correcting Output Codes of Kernel Machines. IEEE Transactions on Neural Networks, pages 45-54, 2004.
24. O. Pujol, P. Radeva, and J. Vitria. Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6):1007-1012, 2006.
25. S. T. Roweis and L. K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290(5500):2323-2326, 2000.
26. R. E. Schapire. A Brief Introduction to Boosting. In IJCAI, pages 1401-1406, 1999.
27. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290(5500):2319-2323, 2000.
28. S. Thrun and J. O'Sullivan. Discovering Structure in Multiple Learning Tasks: The TC Algorithm. In ICML, pages 489-497, 1996.
29. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 11:3371-3408, 2010.
30. Z.Wen andW. Yin. A Feasible Method for Optimization with Orthogonality Constraints. Technical report, Rice University, 2010.
31. Y Xue, X Liao, L Carin, and B Krishnapuram. Multi-Task Learning for Classification with Dirichlet Process Priors. Journal of Machine Learning Research, 8:35-63, 2007.
32. Y. Zhang and D.-Y. Yeung. A Convex Formulation for Learning Task Relationships in Multi-Task Learning. In UAI, pages 733-442, 2010.
33. J. Zhou and C. Y. Suen. Unconstrained Numeral Pair Recognition Using Enhanced Error Correcting Output Coding: A Holistic Approach. In ICDAR, pages 484-488, 2005.