Abstract
This work presents a learning system for the classification of multivariate time series. This classification is useful in domains such as biomedical signals [9], continuous systems diagnosis [2] or data mining in temporal databases [3] .
This work has been supported by the Spanish CYCIT project TAP 99-0344 and the “Junta de Castilla y León” project VA101/01
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Diez, J.J.R., González, C.J.A. (2003). Building RBF Networks for Time Series Classification by Boosting. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_6
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