Abstract
The paper presents a new version on the mini-models method (MM-method). Generally, the MM-method identifies not the full global model of a system but only a local model of the neighborhood of the query point of our special interest. It is an instance-based learning method similarly as the k-nearest algorithm, GRNN network or RBF network but its idea is different. In the MM-method the learning process is based on a group of points that is constrained by a polytope. The first MM-method was described in previous publications of authors. In this paper a new version of the MM-method is presented. In comparison to the previous version it was extended by local dimensionality reduction. As experiments have shown this reduction not only simplifies local models but also in most cases allows for increasing the local model precision.
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Piegat, A., Pietrzykowski, M. (2016). Local Modeling with Local Dimensionality Reduction: Learning Method of Mini-Models. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_32
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DOI: https://doi.org/10.1007/978-3-319-39384-1_32
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