Evolutionary learning of dynamic naive bayesian classifiers

Journal of Automated Reasoning

Volumn 45, Issue 1, 2010, Pages 21-37

  Palacios Alonso, Miguel A ^a   Brizuela, Carlos A ^a   Sucar, L Enrique ^b  

^a CENTRO DE INVESTIGACIÓN CIENTÍFICA Y DE EDUCACIÓN SUPERIOR DE ENSENADA   (Mexico)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

Dynamic Bayesian networks; Genetic algorithms; Gesture recognition; Naive Bayes classifier

Indexed keywords

CLASSIFICATION ACCURACY; DATA SETS; DESIGN METHODOLOGY; DESIGN PROBLEMS; DYNAMIC BAYESIAN NETWORK; DYNAMIC BAYESIAN NETWORKS; DYNAMICAL PROCESS; ENCODING SCHEMES; EVOLUTIONARY LEARNING; EVOLUTIONARY OPTIMIZATION ALGORITHM; GENETIC OPERATORS; GROUPING PROBLEM; HAND GESTURE; NAIVE BAYES CLASSIFIERS; NAIVE BAYESIAN CLASSIFIER; NATURAL EXTENSION; NETWORK DESIGN; NETWORK STRUCTURES; TEMPORAL RELATION; VOICE RECOGNITION;

CLASSIFIERS; DESIGN; DISTRIBUTED PARAMETER NETWORKS; ENCODING (SYMBOLS); GESTURE RECOGNITION; HIDDEN MARKOV MODELS; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 77951878850     PISSN: 01687433     EISSN: 15730670     Source Type: Journal    
DOI: 10.1007/s10817-009-9130-0     Document Type: Conference Paper

References (22)

1. Aviles-Arriaga, H.H., Sucar, L.E., Mendoza, C.E., Vargas, B.: Visual recognition of gestures using dynamic naive bayesian classifiers. In: The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003, pp. 133-138. IEEE Computer Society, Washington, DC, USA (2003)
2. Aviles-Arriaga, H.H., Sucar, L.E., Mendoza, C.E.: Visual recognition of similar gestures. In: ICPR '06: Proceedings of the 18th International Conference on Pattern Recognition, pp. 1100-1103. IEEE Computer Society, Washington, DC, USA (2006)
3. Cameron, P.J.: Combinatorics: Topics, Techniques, Algorithms. Cambridge University Press, Cambridge (1994)
4. Devore, J.L.: Probability and Statistics for Engineering and the Sciences. Wadsworth, Belmont (1995)
5. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Germany (2003)
6. E Falkenauer 1994 A new representation and operators for genetic algorithms applied to grouping problems Evol. Comput. 2 2 123 144 10.1162/evco.1994.2.2.123
7. Friedman, N.: The bayesian structural em algorithm. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 129-138. Morgan Kaufmann, San Francisco, CA (1998)
8. N Friedman D Geiger M Goldszmidt 1997 Bayesian network classifiers Mach. Learn. 29 2-3 131 163 0892.68077 10.1023/A:1007465528199 (Pubitemid 127510036)
9. Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI-98), pp. 139-147. Morgan Kaufmann, San Francisco, CA (1998)
10. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Massachusetts (1989)
11. P Larrañaga M Poza 1996 Structure learning of bayesian networks by genetic algorithms: A. performance analysis of control parameters IEEE J. Pattern Anal. Mach. Intell. 18 9 912 926 10.1109/34.537345 (Pubitemid 126772833)
12. Martínez, M., Sucar, L.E.: Learning dynamic naive bayesian classifier. In: Florida Artificial Intelligence Research Symposium (FLAIRS-21), pp. 655-659. AAAI, Menlo Park, California (2008)
13. Martinez-Arroyo, M., Sucar, L.E.: Learning an optimal naive bayes classifier. In: ICPR '06: Proceedings of the 18th International Conference on Pattern Recognition, pp. 1236-1239. IEEE Computer Society, Washington, DC, USA (2006)
14. Myers, J.W., Laskey, K.B., DeJong, K.A.: Learning bayesian networks from incomplete data using evolutionary algorithms. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 458-465. Morgan Kaufmann, Orlando, Florida, USA (1999)
15. Palacios-Alonso, M.A., Brizuela, C.A., Sucar, L.: Evolutionary learning of dynamic naive bayesian classifiers. In: Florida Artificial Intelligence Research Symposium (FLAIRS-21), pp. 660-665. AAAI, Menlo Park, California (2008)
16. Pazzani, M.: Searching for dependencies in bayesian classifiers. In: Learning from Data: Artificial Intelligence and Statistics V., pp. 239-248. Springer, New York (1996)
17. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA, USA (1988)
18. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Readings in Speech Recognition, IEEE Proceedings, pp. 257-284. Morgan Kaufmann Publishers, San Francisco (1989)
19. Rechenberg, I.: Evolutionstrategie: Optimieriung Technisher Systeme Nach Prinzipien der Biologisten Evolution. Frommann-Holzboog, Stuggart, Germany (1973)
20. BJ Ross E Zuviria 2007 Evolving dynamic bayesian networks with multi-objective genetic algorithms Appl. Intell. 26 1 13 23 10.1007/s10489-006- 0002-6 (Pubitemid 46154564)
21. LE Sucar DF Gillies DA Gillies 1994 Probabilistic reasonig in higth-level vision Image Vis. Comput. 12 1 42 60 10.1016/0262-8856(94)90054-X
22. Wong, M.L., Lee, S.Y., Leung, K.S.: A hybrid approach to learn bayesian networks using evolutionary programming. In: CEC '02: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress, pp. 1314-1319. IEEE Computer Society, Washington, DC, USA (2002)