Abstract
In this paper, we show how an efficient ant based algorithm, called API and initially designed to perform real parameter optimization, can be adapted to the difficult problem of Hidden Markov Models training. To this aim, a transformation of the search space that preserves API’s vectorial moves is introduced. Experiments are conducted with various temporal series extracted from images.
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References
Douglas, B.P.: Training of hmm recognizers by simulated annealing. In: Proceedings of ICASSP 1985, pp. 13–16 (1985)
Chen, T.Y., Mei, X.D., Pan, J.S., Sun, S.H.: Optimization of HMM by the tabu search algorithm. Journal of Information science and engineering 20, 949–957 (2004)
Slimane, M., Venturini, G., Asselin de Beauville, J.P., Brouard, T., Brandeau, A.: Optimizing hidden Markov models with a genetic algorithm. In: Alliot, J.-M., Ronald, E., Lutton, E., Schoenauer, M., Snyers, D. (eds.) AE 1995. LNCS, vol. 1063, pp. 384–396. Springer, Heidelberg (1996)
Slimane, M., Venturini, G., Asselin de Beauville, J.P., Brouard, T.: Hybrid genetic learning of hidden markov models for time series prediction. In: Biomimetic approaches in management science, Kluwer Academics, Dordrecht (1998)
Monmarché, N., Venturini, G., Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16, 937–946 (2000)
Cappé, O.: Ten years of HMM. http://ww.tsi.enst.fr/~cappe/docs/hmmbib.html (2001)
Fine, S., Singer, Y., Tishby, N.: The Hierarchical Hidden Markov Model: Analysis and applications. Machine Learning 32, 41–62 (1998)
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE 77, 257–286 (1989)
Forney, G.: The Viterbi algorithm. Proceedings of IEEE 61, 268–278 (1973)
Baum, L., Petrie, T., Soules, G., Weiss, N.: A maximization technique occuring in the statistical analysis of probabilistic functions of markov chains. Ann. Math. Stat. 41, 164–171 (1970)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stygmergy. Future Generation Computer Systems 16, 851–871 (2000)
Dorigo, M., Di Caro, G.: The Ant Colony Optimization Meta-Heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, Belgium, pp. 11–32. McGraw-Hill, London, UK (1999); also available as Tech.Rep.IRIDIA/99-1, Université Libre de Bruxelles, Belgium
Dorigo, M., Gambardella, L.: Ant Colony Sytem: A cooperative learning approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)
Stützle, T., Hoos, H.: MAX − MIN Ant System and local search for the Traveling Salesman Problem. In: IEEE (ed.) Proceedings of the fourth International Conference on Evolutionary Computation (ICEC), pp. 308–313. IEEE Press, Los Alamitos (1997)
Stützle, T., Dorigo, M.: ACO algorithms for the Quadratic Assignment Problem. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, pp. 33–50. McGraw-Hill, London, UK (1999)
T’Kindt, V., Monmarché, N., Tercinet, F., Laügt, D.: An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. European Journal of Operational Research 142, 250–257 (2002)
Ying, K.C., Liao, C.J.: An ant colony system for permutation flow-shop sequencing. Computers & Operations Research 31, 791–801 (2004)
Bilchev, G., Parmee, I.: The ant colony metaphor for searching continuons design spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 24–39. Springer, Heidelberg (1995)
Li, S., Liu, Z.: General CAC approach using novel ant algorithm training based neural network. Proceedings of the International Joint Conference on Neural Networks 3, 1885–1888 (1999)
Dréo, J., Siarry, P.: Continuous interacting ant colony algorithm based on dense heterarchy. In: Future Generation Computer Systems (in press 2004)
Fresneau, D.: Individual foraging and path fidelity in a ponerine ant. Insectes Sociaux, Paris 32, 109–116 (1985)
Monmarché, N., Venturini, G., Slimane, M.: On how Pachycondyla apicalis ants suggest a new search algorithm. Future Generation Computer Systems 16, 937–946 (2000)
Monmarché, N.: Algorithmes de fourmis artificielles: applications à la classification et à l’optimisation. Thèse de doctorat, Laboratoire d’Informatique, Université de Tours (2000)
Deneubourg, J., Goss, S., Pasteels, J., Fresneau, D., Lachaud, J.: Self-organization mechanisms in ant societies (ii): learning in foraging and division of labor. In: Pasteels, J., Deneubourg, J. (eds.) From individual to collective behavior in social insects, Experientia supplementum, vol. 54, pp. 177–196. Bikhauser Verlag (1987)
Soukhal, A., Monmarché, N., Laügt, D., Slimane, M.: How hidden markov models can help artificial ants to optimize. In: Proceedings of the Optimization by Building and Using Probabilistic Models workshop, Genetic and Evolutionary Computation Conference, pp. 226–229 (2001)
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: IEEE workshop on Applications of Computer Vision, Florida (1994)
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Aupetit, S., Monmarché, N., Slimane, M., Liardet, P. (2006). An Exponential Representation in the API Algorithm for Hidden Markov Models Training. In: Talbi, EG., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2005. Lecture Notes in Computer Science, vol 3871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11740698_6
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DOI: https://doi.org/10.1007/11740698_6
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