Effectiveness of error correcting output codes in multiclass learning problems

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

Volumn 1857 LNCS, Issue , 2000, Pages 107-116

  Masulli, Francesco ^a,b   Valentini, Giorgio ^a,b  

^a INFM   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); MACHINE LEARNING; LEARNING SYSTEMS;

DECOMPOSITION METHODS; DECOMPOSITION SCHEME; ERROR CORRECTING OUTPUT CODE; GENERALIZATION CAPABILITY; LEARNING MACHINES; MULTI-CLASS LEARNING; MULTI-LAYER PERCEPTRONS; MULTICLASS CLASSIFICATION PROBLEMS;

ERRORS;

EID: 84867071286     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45014-9_10     Document Type: Conference Paper

References (22)

1. E. Alpaydin and E. Mayoraz. Combining linear dichotomizers to construct nonlinear polychotomizers. Technical report, IDIAP-RR 98-05 - Dalle Molle Institute for Perceptual Artificial Intelligence, Martigny (Switzerland) 1998. ftp://ftp.idiap.ch/pub/reports/1998/rr98-05.ps.gz.
2. R. Anand, G. Mehrotra, C.K. Mohan and S. Ranka. Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks, 6:117-124, 1995.
3. R.C. Bose and D.K. Ray-Chauduri. On a class of error correcting binary group codes. Information and Control, (3):68-79, 1960.
4. V. N. Cherkassky and F. Mulier. Learning from data: Concepts, Theory and Methods. Wiley & Sons, New York, 1998.
5. T. Dietterich and G. Bakiri. Error - correcting output codes: A general method for improving multiclass inductive learning programs. In Proceedings of AAAI-91, pages 572-577. AAAI Press / MIT Press, 1991.
6. T. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, (2):263-286, 1995.
7. T.G. Dietterich. Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation, 7 (10):1895-1924, 1998.
8. B.S. Everitt. The analysis of contingency tables. Chapman and Hall, London, 1977.
9. S. Hashem. Optimal linear combinations of neural networks. Neural Computation, 10:599-614, 1997.
10. J.R. Quinlan. C4.5 Programs for Machine Learning. Morgan Kauffman, 1993.
11. E. Kong and T. Dietterich. Error - correcting output coding correct bias and variance. In The XII International Conference on Machine Learning, pages 313-321, San Francisco, CA, 1995. Morgan Kauffman.
12. S. Lin and D.J. Jr. Costello. Error Control Coding: Fundamentals and Applications. Prentice-Hall, Englewood Cliffs, 1983.
13. F. Masulli and G. Valentini. Comparing decomposition methods for classification. In KES'2000, Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Brighton, England. (in press).
14. F. Masulli and G. Valentini. Parallel Non linear Dichotomizers. In IJCNN2000, The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy. (in press).
15. E. Mayoraz and M. Moreira. On the decomposition of polychotomies into dichotomies. In The XIV International Conference on Machine Learning, pages 219-226, Nashville, TN, July 1997.
16. C.J. Merz and P.M. Murphy. UCI repository of machine learning databases, 1998. www.ics.uci.edu/mlearn/MLRepository.html.
17. M.P. Perrone and L.N. Cooper. When networks disagree: ensemble methods for hybrid neural networks. In Mammone R.J., editor, Artificial Neural Networks for Speech and Vision, pages 126-142. Chapman & Hall, London, 1993.
18. W.W. Peterson and E.J. Jr. Weldon. Error correcting codes. MIT Press, Cambridge, MA, 1972.
19. D.E. Rumelhart, G.E. Hinton and R.J. Williams. Learning internal reperesentations by error propagation. In Rumelhart D.E., McClelland J.L., editor, Parallel Distributed Processing: Explorations in the Microstructure of Conition, volume 1, chapter 8. MIT Press, Cambridge, MA, 1986.
20. T. J. Sejnowski and C. R. Rosenberg. Parallel networks that learn to pronounce english text. Journal of Artificial Intelligence Research, (1):145-168, 1987.
21. G. Valentini. Metodi scompositivi per la classificazione. Master's thesis, Dipartimento di Informatica e Scienze Informazione - Università di Genova, Genova, Italy, 1999.
22. G. Valentini and F. Masulli. NEURObjects, a set of library classes for neural networks development. In Proceedings of the third International ICSC Symposia on Intelligent Industrial Automation (IIA'99) and Soft Computing (SOCO'99), pages 184-190, Millet, Canada, 1999. ICSC Academic Press.