Abstract
In this paper, we propose a generic description of the concept lattice as classifier in an iterative recognition process. We also present the development of a new structural signature adapted to noise context. The experimentation is realized on the noised symbols of GREC database [4]. Our experimentation presents a comparison with the two classical numerical classifiers that are the bayesian classifier and the nearest neighbors classifier and some comparison elements for an iterative process.
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Barbut, M., Monjardet, B.: Ordres et classifications: Algèbre et combinatoire (tome II). In: Hachette, Paris (1970)
Bordat, J.: Calcul pratique du treillis de Galois d’une correspondance. Math. Sci. Hum. 96, 31–47 (1986)
Geibel, P., Wysotzki, F.: Learning relational concepts with decision trees. In: Saitta, L. (ed.) Machine Learning: Proceedings of the Thirteenth International Conference, pp. 166–174. Morgan Kaufmann Publishers, San Francisco (1996)
GREC. Symbol images database GREC 2003 (Graphics RECognition), Last access 09/10/2007 (2003), http://www.cvc.uab.es/grec2003/symreccontest/index.htm
Guillas, S., Bertet, K., Ogier, J.-M.: Concept lattice classifier: A first step towards an iterative process of recognition of noised graphic objects. In: Ben Yahia, S., Mephu Nguifo, E. (eds.) Fourth International Conference on Concept Lattices and their Applications (CLA 2006), Hammamet, Tunisia, pp. 257–263 (2006)
Guillas, S., Bertet, K., Ogier, J.-M.: A generic description of the concept lattices classifier: Application to symbol recognition. In: Graphics Recognition: Ten Years Review and Future Perspectives - Selected papers from GREC 2005, Hong Kong, China, August 2005. LNCS, vol. 3926, pp. 47–60. Springer, Berlin / Heidelberg (2006) (Revised and extended version of paper first presented at Sixth IAPR International Workshop on Graphics Recognition (GREC 2005))
Kanungo, T., et al.: Document degradation models: Parameter estimation and model validation. In: IAPR Workshop on machine vision applications, Kawasaki (Japan), pp. 552–557 (1994)
Krus, D., Fuller, E.: Computer assisted multicrossvalidation in regression analysis. Educational and Psychological Measurement 42, 187–193 (1982)
Mephu Nguifo, E., Njiwoua, P.: Treillis des concepts et classification supervisée. In: Technique et Science Informatiques, RSTI, Hermès - Lavoisier, Paris, France, vol. 24(4), pp. 449–488 (2005)
Morvan, M., Nourine, L.: Simplicial elimination shemes, extremal lattices and maximal antichains lattice. Order 13(2), 159–173 (1996)
Rakotomalala, R.: Graphes d’induction. PhD thesis, Université Claude Bernard, Lyon I, Décembre (1997)
Tabbone, S., Wendling, L.: Adaptation de la transformée de Radon pour la recherche d’objets à niveaux de gris et de couleurs. In: Technique et Science Informatiques, RSTI, Hermès - Lavoisier, Paris, France, vol. 22(9), pp. 1139–1166 (2003)
Teague, M.: Image analysis via the general theory of moments. Journal of Optical Society of America (JOSA) 70, 920–930 (2003)
Wille, R.: Restructuring lattice theory: An approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered sets, pp. 445–470. Reidel, Dordrecht-Boston (1982)
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Guillas, S., Bertet, K., Ogier, JM. (2008). Towards an Iterative Classification Based on Concept Lattice. In: Yahia, S.B., Nguifo, E.M., Belohlavek, R. (eds) Concept Lattices and Their Applications. CLA 2006. Lecture Notes in Computer Science(), vol 4923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78921-5_18
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DOI: https://doi.org/10.1007/978-3-540-78921-5_18
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