Separability of ternary codes for sparse designs of error-correcting output codes

Pattern Recognition Letters

Volumn 30, Issue 3, 2009, Pages 285-297

  Escalera, Sergio ^a,b   Pujol, Oriol ^a,b   Radeva, Petia ^a,b  

^a Barcelona Perception Computing Lab   (Spain)

^b UNIVERSITY OF BARCELONA   (Spain)

Author keywords

Embedding of dichotomizers; Error correcting output codes; Sparse random designs

Indexed keywords

CLASSIFIERS; CODES (SYMBOLS); DECODING; DESIGN; LEARNING SYSTEMS;

CATEGORIZATION PROBLEMS; COMBINING BINARY CLASSIFIERS; DECODING STRATEGIES; DESIGN CODING; EMBEDDING OF DICHOTOMIZERS; OUTPUT CODES; PERFORMANCE IMPROVEMENTS; RANDOM CODING; SPARSE RANDOM DESIGNS; TERNARY CODES; UCI MACHINE LEARNING REPOSITORIES;

MACHINE DESIGN;

EID: 57349128872     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.10.002     Document Type: Article

References (20)

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.
2. Asuncion, A., Newman, D., 2007. In: UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences. .
3. 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.
4. Dietterich T., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2 (1995) 263-282
5. Escalera, S., Pujol, O., Radeva, P., 2006. Decoding of ternary error correcting output codes. In: CIARP, vol. 4225, pp. 753-763.
6. Escalera S., Pujol O., and Radeva P. Boosted landmarks of contextual descriptors and Forest-ECOC: A novel framework to detect and classify objects in clutter scenes. Pattern Recognition Lett. 28 13 (2007) 1759-1768
7. Escalera, S., Pujol, O., Radeva, P., 2008. Loss-weighted decoding for error-correcting output codes. In: Internat. Conf. on Computer Vision Theory and Applications, vol. 2, pp. 117-122.
8. Friedman J., Hastie T., and Tibshirani R. Additive logistic regression: A statistical view of boosting. Ann. Statist. 38 (1998) 337-374
9. Ghani, R., 2001. Combining labeled and unlabeled data for text classification with a large number of categories. In: Internat. Conf. Data Mining, pp. 597-598.
10. Hastie, T., Tibshirani, R., 1998. Classification by pairwise grouping. In: NIPS, vol. 26, 451-471.
11. Kittler, J., Ghaderi, R., Windeatt, T., Matas, J., 2001. Face verification using error correcting output codes. In: CVPR, vol. 1, pp. 755-760.
12. Kong, E.B., Dietterich, T.G., 1995. Error-correcting output coding corrects bias and variance. In: ICML, pp. 313-321.
13. Nilsson N.J. Learning Machines (1965), McGraw-Hill
14. Passerini A., Pontil M., and Frasconi P. New results on error correcting output codes of kernel machines. IEEE Trans. Neural Networks 15 1 (2004) 45-54
15. Pujol, O., Radeva, P., Vitrià, J., 2006. Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. In: PAMI, vol. 28, pp. 1001-1007.
16. Rifkin R., and Klautau A. In defense of one-vs-all classification. J. Machine Learn. Res. 5 (2004) 101-141
17. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer
18. Windeatt, T., Ardeshir, G., 2003. Boosted ECOC ensembles for face recognition. In: Internat. Conf. on Visual Information Engineering, pp. 165-168.
19. Windeatt, T., Ghaderi, R., 2003. Coding and decoding for multi-class learning problems. In: Information Fusion, 4, 11-21.
20. Zhou, J., Suen, C., 2005. Unconstrained numeral pair recognition using enhanced error correcting output coding: A holistic approach. In: Proc. Conf. on Document Analysis and Recognition, vol. 1, pp. 484-488.