Abstract
Up-to-date results on the application of Markov models to chromosome analysis are presented. On the one hand, this means using continuous Hidden Markov Models (HMMs) instead of discrete models. On the other hand, this also means to conduct empirical tests on the same large chromosome datasets that are currently used to evaluate state-of-the-art classifiers. It is shown that the use of continuous HMMs allows to obtain error rates that are very close to those provided by the most accurate classifiers.
Work supported by the Valencian “Oficina de Ciència i Tecnologia” under grant CTIDIA/2002/80, the Spanish “Ministerio de Ciencia y Tecnología” under grant TIC2000-1703-CO3-01 and the Argentinian “Ministerio de Educación de la Nación” under fellowship FOMEC 1108.
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Martínez, C., García, H., Juan, A., Casacuberta, F. (2003). Chromosome Classification Using Continuous Hidden Markov Models. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_58
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DOI: https://doi.org/10.1007/978-3-540-44871-6_58
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