Modular neural network task decomposition via entropic clustering

Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

Volumn 1, Issue , 2006, Pages 62-67

  Santos, Jorge M ^a   Alexandre, Luís A ^b   De Sá, Joaquim Marques ^c  

^a INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

^b IT Networks and Multimedia Group   (Portugal)

^c INEB INSTITUTO DE ENGENHARIA BIOMÉDICA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DECISION THEORY; LEARNING SYSTEMS;

ENTROPIC CLUSTERING; MODULAR NEURAL NETWORK TASK DECOMPOSITION; MONOLITHIC NEURAL NETWORKS; WEIGHT COUPLING;

NEURAL NETWORKS;

EID: 34547502901     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2006.198     Document Type: Conference Paper

References (18)

1. Jorge M. Santos, Joaquim Marques de Sá, and Luis A. Alexandre. LEGClust - a clustering algorithm based on layered entropie subgraphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. submitted.
2. Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2):105-139, 1999.
3. Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2):139-157, 2000.
4. Josef Kittler, Mohamad Hatef, Robert P. W. Duin, and Jiri Matas. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226-239, 1998.
5. Erin L. Allwein, Robert E. Schapire, and Yoram Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. In Proc. 17th International Conf on Machine Learning, pages 9-16. Morgan Kaufmann, San Francisco, CA, 2000.
6. L. A. Alexandre, A. C. Campilho, and M. Kamel. Bounds for the average generalization error of the mixture of experts neural network. In 5th International Workshop on Statistical Techniques in Pattern Recognition, LNCS 3138, pages 618-625, 2004.
7. R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton. Adaptive mixtures of local experts. Neural Computation, (3):pp.79-87, 1991.
8. G. Auda and M. Kamel. Modular neural network classifiers: A comparative study. Intel. Robotic Systems, 21:117-129, 1998.
9. M. Jordan and R. Jacobs. Hierarchical mixture of experts and the EM algorithm. Neural Computation, (6):181-214, 1994.
10. R. Jacobs, F. Peng, and M. Tanner. A bayesian approach to model selection in hierarchical mixtures-of-experts architectures. Neural Networks, 10(2):231-241, 1997.
11. R. Vilalta, M. K. Achari, and C. F. Eick. Class decomposition via clustering: a new framework for lowvariance classifiers. In Third IEEE International Conference on Data Mining, pages 673-676, 2003.
12. L. A. Alexandre, A. C. Campilho, and M. Kamel. A probabilistic model for the cooperative modular neural network. In Proceedings of the IbPRIA 2003, LNCS 2652, pages 11-18, 2003.
13. Abdellatif Ennaji, Arnaud Ribert, and Yves Lecourtier. From data topology to a modular classifier. International Journal on Document Analysis and Recognition, 6(1):1-9, 2003.
14. Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, volume 14, 2001.
15. A. W. Bowman and A. Azzalini. Applied Smooting Techniques for Data Analysis. London:Oxford University Press, 1997.
16. Joaquim Marques de Sá. Applied statistics using SPSS, STATISTICA andMATLAB. Springer, 2003.
17. C. Blake, E. Keogh, and C. Merz. UCI repository of machine learning databases, 1998.
18. M. Forina and C. Armanino. Eigenvector projection and simplified non-linear mapping of fatty acid content of Italian olive oils. Ann. Chim. (Rome), 72:127-155, 1981.