Constructing multiclass learners from binary learners: A simple black-box analysis of the generalization errors

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

Volumn 3734 LNAI, Issue , 2005, Pages 135-147

  Fakcharoenphol, Jittat ^a   Kijsirikul, Boonserm ^b  

^a KASETSART UNIVERSITY   (Thailand)

^b CHULALONGKORN UNIVERSITY   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; CLASSIFICATION (OF INFORMATION); DECODING; ERROR ANALYSIS; GRAPH THEORY; PROBLEM SOLVING;

CODING MATRIX; DECISION DIRECTED ACYCLIC GRAPHS; GENERALIZATION ERRORS; MULTICLASS LEARNERS;

LEARNING SYSTEMS;

EID: 33646535265     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564089_12     Document Type: Conference Paper

References (25)

1. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 (1997) 119-139
2. Vapnik, V.: Statistical Learning Theory. Wiley (1998)
3. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20 (1995) 273-297
4. Friedman, J.H.: Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University (1996)
5. Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: NIPS '97: Proceedings of the 1997 conference on Advances in neural information processing systems 10, Cambridge, MA, USA, MIT Press (1998) 507-513
6. Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Advance in Neural Information Processing System. Volume 12., MIT Press (2000)
7. Kreßel, U.H.G.: Pairwise classification and support vector machines. In: Advances in kernel methods: support vector learning. MIT Press, Cambridge, MA, USA (1999) 255-268
8. Kijsirikul, B., Ussivakul, N., Meknavin, S.: Adaptive directed acyclic graphs for multiclass classification. In: PRICAI 2002. (2002) 158-168
9. Dietterich, T.G., Bakiri, G.: Error-correcting output codes: a general method for improving multiclass inductive learning programs. In Dean, T.L., McKeown, K., eds.: Proceedings of the Ninth AAAI National Conference on Artificial Intelligence, Menlo Park, CA, AAAI Press (1991) 572-577
10. Guruswami, V., Sahai, A.: Multiclass learning, boosting, and error-correcting codes. In: Computational Learning Theory. (1999) 145-155
11. Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1 (2001) 113-141
12. Har-Peled, S., Roth, D., Zimak, D.: Constraint classification: A new approach to multiclass classification and ranking. In: NIPS. (2003)
13. Bar-Hillel, A., Weinshall, D.: Learning with equivalence constraints, and the relation to multiclass learning. In: COLT. (2003)
14. Fakcharoenphol, J.: A note on random DDAG. Manuscript (2003)
15. Schapire, R.E., Freund, Y., Bartlett, P.L., Lee, W.S.: Boosting the margin: a new explanation for the effectiveness of voting methods. Annals of Statistics 26 (1998) 1651-1686
16. Paugam-Moisy, H., Elisseeff, A., Guermeur, Y.: Generalization performance of multiclass discriminant models. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, Como, Italy, July 24-27, 2000, Volume 4, IEEE (2000)
17. Bennett, K.P., Cristianini, N., Shawe-Taylor, J., Wu, D.: Enlarging the margins in perceptron decision trees. Mach. Learn. 41 (2000) 295-313
18. Shawe-Taylor, J., Bartlett, P.L., Williamson, R.C., Anthony, M.: A framework for structural risk minimisation. In: COLT '96: Proceedings of the ninth annual conference on Computational learning theory, ACM Press (1996) 68-76
19. Ben-David, S., Cesa-Bianchi, N., Haussler, D., Long, P.M.: Characterizations of learnability for classes of {0, :., n}-valued functions. J. Comput. Syst. Sci. 50 (1995) 74-86
20. Natarajan, B.K.: On learning sets and functions. Mach. Learn. 4 (1989) 67-97
21. Vapnik, V.N., Chervonenkis, A.Y.: On the uniform convergence of relative frequencies of events to their probabilities. Theoret. Probi. and its Appl. 16 (1971) 264-280
22. Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Learnability and the vapnik-chervonenkis dimension. J. ACM 36 (1989) 929-965
23. Phetkaew, T., Kijsirikul, B., Rivepiboon, W.: Reordering adaptive directed acyclic graphs for multiclass support vector machines. In: Proceedings of the Third International Conference on Intelligent Technologies (InTech 2002). (2002)
24. Klautau, A., Jevtić;, N., Orlitsky, A.: On nearest-neighbor error-correcting output codes with application to all-pairs multiclass support vector machines. J. Mach. Learn. Res. 4 (2003) 1-15
25. Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5 (2004) 101-141