Abstract
We present in this paper a hybrid neuro-symbolic system called “CASEP2”, which combines the case-based reasoning with an adequate artificial neural network “M-SOM-ART” for sequence classification or prediction task. In CASEP2, we present a new case modelling by dynamic covariance matrices. This model takes into account the temporal dynamics contained in the sequences and allows to avoid problems related to the comparison of different length sequences. In the CBR cycle, one neural network is used during the retrieval phase for indexing the case base and another is used during the reuse phase in order to provide the target case solution.
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Zehraoui, F., Kanawati, R., Salotti, S. (2004). CASEP2: Hybrid Case-Based Reasoning System for Sequence Processing. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_33
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DOI: https://doi.org/10.1007/978-3-540-28631-8_33
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