Decomposition Method for Neural Multiclass Classification Problem
In this article we are going to discuss the improvement
of the multi classes- classification problem using multi layer
Perceptron. The considered approach consists in breaking down the
n-class problem into two-classes- subproblems. The training of each
two-class subproblem is made independently; as for the phase of test,
we are going to confront a vector that we want to classify to all two
classes- models, the elected class will be the strongest one that won-t
lose any competition with the other classes. Rates of recognition
gotten with the multi class-s approach by two-class-s decomposition
are clearly better that those gotten by the simple multi class-s
approach.
[1] V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-
Verlag, London, UK,1995.
[2] U. H.-G. Kreßel. "Pairwise classification and support vector machines".
In B. Scholkopf,C. J. C. Burges, and A. J. Smola, editors, Advances in
Kernel Methods: Support Vector Learning, pages 255-268. The MIT
Press, Cambridge, MA, 1999.
[3] J. F. Jodouin, Les réseaux de neurones, principes et définitions. Hermès,
1994.
[4] P.W. Frey and D.J. Slate "Letter Recognition Using Holland-style
Adaptive Classifiers". Machine Learning Vol 6 #2, 1991.
[5] H. White, "Connectionist nonparametric regression: Multilayered
feedforward networks can learn arbitrary mapping", Neural Networks, 3,
pp.535-550, 1990.
[6] Z. Tang, P. Fishwick "Feed-forward Neural Nets as Models for Time
Series Forecasting", ORSA Journal of computing 5 (4) pp 374-386,
1993.
[7] W. CHEN, S. CHEN, C. LIN, "A speech recognition method based on
the sequential multi-layer perceptrons" Neural Networks, Vol. 9, No. 4,
pp. 655-669,1996.
[8] H. Abdi, Les réseaux de neurones. Sciences et technologies de la
connaissance. Presse Universitaire de Grenoble, France, 1994.
[9] V. Kecman. Learning and Soft Computing, Support Vector Machines,
Neural Networks, and Fuzzy Logic Models. England : The MIT Press,
2001.
[10] A. Cornuéjols, L. Miclet, Y. Kodratoff. Apprentissage artificiel
Concepts et algorithmes. France : Editions Eyrolles, 2003.
[11] A. Shigeo "Analysis of Multiclass Support Vector Machines", Graduate
School of Science and Technology Kobe University, Kobe, Japan,
Available: www2.kobe-u.ac.jp/~abe/pdf/cimca2003.pdf
[12] J. C. Platt, N. Cristianini, and J. Shawe-Taylor. "Large margin DAGs for
multiclass classification". In S. A. Solla, T. K. Leen, and K.-R.M¨uller,
editors, Advances in Neural InformationProcessing Systems 12, pages
547-553. The MIT Press, 2000.
[1] V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-
Verlag, London, UK,1995.
[2] U. H.-G. Kreßel. "Pairwise classification and support vector machines".
In B. Scholkopf,C. J. C. Burges, and A. J. Smola, editors, Advances in
Kernel Methods: Support Vector Learning, pages 255-268. The MIT
Press, Cambridge, MA, 1999.
[3] J. F. Jodouin, Les réseaux de neurones, principes et définitions. Hermès,
1994.
[4] P.W. Frey and D.J. Slate "Letter Recognition Using Holland-style
Adaptive Classifiers". Machine Learning Vol 6 #2, 1991.
[5] H. White, "Connectionist nonparametric regression: Multilayered
feedforward networks can learn arbitrary mapping", Neural Networks, 3,
pp.535-550, 1990.
[6] Z. Tang, P. Fishwick "Feed-forward Neural Nets as Models for Time
Series Forecasting", ORSA Journal of computing 5 (4) pp 374-386,
1993.
[7] W. CHEN, S. CHEN, C. LIN, "A speech recognition method based on
the sequential multi-layer perceptrons" Neural Networks, Vol. 9, No. 4,
pp. 655-669,1996.
[8] H. Abdi, Les réseaux de neurones. Sciences et technologies de la
connaissance. Presse Universitaire de Grenoble, France, 1994.
[9] V. Kecman. Learning and Soft Computing, Support Vector Machines,
Neural Networks, and Fuzzy Logic Models. England : The MIT Press,
2001.
[10] A. Cornuéjols, L. Miclet, Y. Kodratoff. Apprentissage artificiel
Concepts et algorithmes. France : Editions Eyrolles, 2003.
[11] A. Shigeo "Analysis of Multiclass Support Vector Machines", Graduate
School of Science and Technology Kobe University, Kobe, Japan,
Available: www2.kobe-u.ac.jp/~abe/pdf/cimca2003.pdf
[12] J. C. Platt, N. Cristianini, and J. Shawe-Taylor. "Large margin DAGs for
multiclass classification". In S. A. Solla, T. K. Leen, and K.-R.M¨uller,
editors, Advances in Neural InformationProcessing Systems 12, pages
547-553. The MIT Press, 2000.
@article{"International Journal of Information, Control and Computer Sciences:56392", author = "H. El Ayech and A. Trabelsi", title = "Decomposition Method for Neural Multiclass Classification Problem", abstract = "In this article we are going to discuss the improvement
of the multi classes- classification problem using multi layer
Perceptron. The considered approach consists in breaking down the
n-class problem into two-classes- subproblems. The training of each
two-class subproblem is made independently; as for the phase of test,
we are going to confront a vector that we want to classify to all two
classes- models, the elected class will be the strongest one that won-t
lose any competition with the other classes. Rates of recognition
gotten with the multi class-s approach by two-class-s decomposition
are clearly better that those gotten by the simple multi class-s
approach.", keywords = "Artificial neural network, letter-recognition, Multi
class Classification, Multi Layer Perceptron.", volume = "2", number = "3", pages = "782-4", }