Abstract
The paper presents the mini-models’ method (MM-method) based on n-dimensional simplex. Its learning algorithm is in some respects similar to the method of k-nearest neighbors. Both methods use samples only from the local neighborhood of the query point. In the mini-model method, group of points which are used in the model-learning process is constrained by a polytope (n-simplex) area. The MM-method can on a defined local area use any approximation algorithm to determine the mini-model and to compute its answer for the query point. The article describes a learning technique for the MM-method and presents experiment results that show effectiveness of mini-models.
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Pietrzykowski, M., Piegat, A. (2015). Geometric Approach in Local Modeling: Learning of Mini-models Based on n-Dimensional Simplex. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_41
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DOI: https://doi.org/10.1007/978-3-319-19369-4_41
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