Abstract
In this paper we propose applying multidimensional decompositions for modeling improvement. Results generated by different models usually include both wanted and destructive components. Many of the components are common to all the models. Our aim is to find the basis variables with the positive and the negative influence on the modeling task. It will be perofrmed with multidimensional transforamtions such as ICA and PCA.
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Szupiluk, R., Wojewnik, P., Zabkowski, T. (2004). Model Improvement by the Statistical Decomposition. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_188
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DOI: https://doi.org/10.1007/978-3-540-24844-6_188
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
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