Minimal design of error-correcting output codes

Pattern Recognition Letters

Volumn 33, Issue 6, 2012, Pages 693-702

  Bautista, Miguel Ángel ^a,b   Escalera, Sergio ^a,b   Baró, Xavier ^a,b,c   Radeva, Petia ^a,b   Vitriá, Jordi ^a,b   Pujol, Oriol ^a,b  

^a Barcelona Perception Computing Lab   (Spain)

^b UNIVERSITY OF BARCELONA   (Spain)

^c UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

Author keywords

Ensemble of classifiers; Error correcting output codes; Multi class classification

Indexed keywords

CODE CONFIGURATION; COMBINING CLASSIFIERS; COMPUTER VISION PROBLEMS; DATA SETS; ENSEMBLE OF CLASSIFIERS; ERROR CORRECTING OUTPUT CODE; EXPONENTIAL NUMBERS; MULTI-CLASS; MULTI-CLASS CLASSIFICATION; OBJECT CATEGORIES;

PATTERN RECOGNITION;

TEACHING;

EID: 84856799903     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.09.023     Document Type: Article

References (32)

1. Allwein, E.; Schapire, R.; Singer, Y. 2002. Reducing multiclass to binary: A unifying approach for margin classifiers. In: JMLR, vol. 1, pp. 113-141. (Pubitemid 33738778)
2. Alpaydin, E.; Mayoraz, E. 1999. Learning error-correcting output codes from data. In: Artificial Neural Networks, 1999. ICANN 99. Ninth Internat. Conf. on (Conf. Publ. No. 470), vol. 2, pp. 743 -748. (Pubitemid 30519857)
3. Asuncion, A.; Newman, D. 2007. UCI machine learning repository, University of California, Irvine, School of Information and Computer Sciences, .
4. S. Baluja, and R. Caruana Removing the genetics from the standard genetic algorithm A. Prieditis, S. Russel, The Internat. Conf. on Machine Learning 1995 1995 Morgan Kaufmann Publishers San Mateo, CA 38 46
5. Casacuberta, J.; Miranda, J.; Pla, M.; Sanchez, S.; Serra, A.; Talaya, J. 2004. On the accuracy and performance of the GeoMobil system. In: Internat. Society for Photogrammetry and Remote Sensing.
6. Crammer, K.; Singer, Y. 2002. On the learnability and design of output codes for multi-class problems. In: Machine Learning, vol. 47, pp. 201-233. (Pubitemid 34126110)
7. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines: And other Kernel-Based Learning Methods 2000 Cambridge University Press
8. J. Demsar Statistical comparisons of classifiers over multiple data sets JMLR 7 2006 1 30 (Pubitemid 43022939)
9. Dietterich, T.; Bakiri, G. 1995. Solving multiclass learning problems via error-correcting output codes. In: JAIR, vol. 2, pp. 263-286.
10. Dietterich, T.; Kong, E. 1995. Error-correcting output codes corrects bias and variance. In: Prieditis, S.; Russell, S. (Eds.), ICML, pp. 313-321.
11. Escalera, S.; Pujol, O.; Radeva, P. 2006. Ecoc-one: A novel coding and decoding strategy. In: Pattern Recognition 2006 18th Internat. Conf. on ICPR, vol. 3, pp. 578 -581. (Pubitemid 46553188)
12. Escalera, S.; Pujol, O.; Radeva, P. 2007. Boosted landmarks and forest ECOC: A novel framework to detect and classify objects in clutter scenes. In: Pattern Recognition Letters, vol. 28, pp. 1759-1768. (Pubitemid 47064476)
13. S. Escalera, A. Fornes, O. Pujol, P. Radeva, G. Sanchez, and J. Llados Blurred shape model for binary and grey-level symbol recognition Pattern Recognit. Lett. 30 2009 1424 1433
14. S. Escalera, O. Pujol, and P. Radeva Error-correcting output codes library J. Mach. Learn. Res. 11 2010 661 664
15. S. Escalera, O. Pujol, and P. Radeva On the decoding process in ternary error-correcting output codes, Trans Pattern Anal. Machine Intell. 99 1 2010
16. A. Fornés, J. Lladós, and G. Sánchez Primitive segmentation in old handwritten music scores Graph. Recognit. 3926 2006 279 290 (Pubitemid 44459599)
17. Friedman, J.; Hastie, T.; Tibshirani, R. 1998. Additive logistic regression: a statistical view of boosting, Dept. of Statistics, Stanford University Technical Report.
18. T. Hastie, and R. Tibshirani Classification by pairwise grouping NIPS 26 1998 451 471
19. J. Holland Adaptation in Natural and Artificial systems: An Analysis with Applications to Biology, Control, and Artificial Intelligence 1975 University of Michigan Press
20. Huang, G.B.; Ramesh, M.; Berg, T.; Miller, E.L. 2007. Labeled faces in the wild: A database for studying face recognition in unconstrained environments, Technical Reports University of Massachusets Amherst, 07-49 (October 2007).
21. W. Hwang, J. Weng, and Y. Zhang Candid covariance-free incremental principal component analysis IEEE Trans. Pattern Anal. Machine Intell. 25 8 2003 1034 1040
22. Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137-1145.
23. A.C. Lorena, and A.C. de Carvalho Evolutionary tuning of svm parameter values in multiclass problems Neurocomputing 71 16-18 2008 3326 3334
24. A. Martinez, R. Benavente, The AR face database. In: Computer Vision Center Technical Report #24, 1998.
25. OSU-SVM-TOOLBOX, .
26. Passerini, A.; Pontil, M.; Frasconi, P. 2004. New results on error correcting output codes of Kernel machines. In: Neural Networks, vol. 15, pp. 45-54.
27. M. Pelikan, D.E. Goldberg, and E. Cantú-Paz Learning Machines 1965 McGraw-Hill
28. Pujol, O.; Radeva, P.; Vitrià, J. 2006. Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. In: Trans. on PAMI, vol. 28, pp. 1001-1007.
29. R. Rifkin, and A. Klautau In defense of one-vs-all classification JMLR 5 2004 101 141
30. C.N. Silla Jr.; and A.A. Freitas A survey of hierarchical classification across different application domains Data Min. Knowl. Discov. 22 1-2 2011 31 72
31. Utschick, W.; Weichselberger, W. 2004. Stochastic organization of output codes in multiclass learning problems. In: Neural Computation, vol. 13, pp. 1065-1102.
32. .