Abstract
Mini-models are local regression models which can be used for the function approximation learning. In the paper, there are presented mini-models based on hyper-spheres and researches were made for linear and nonlinear models with no limitations for the problem input space dimension. Learning of the approximation function based on mini-models is very fast and it proved to have a good accuracy. Mini-models have also very advantageous extrapolation properties. It results from a fact, that they take into account not only samples target values, but also a tendency in the neighbourhood of the question point.
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References
Atkeson, C.G., Moore, A.W., Schaal, S.A.: Locally weighted learning. Artificial Intelligence Review 11, 11–73 (1997)
Cichosz, P.: Learning systems. WNT Publishing House, Warsaw (2000) (in polish)
Hand, D., Mannila, H., Smyth, P.: Principles of data mining. The MIT Press (2001)
Kordos, M., Blachnik, M., Strzempa, D.: Do We Need Whatever More Than k-NN? In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6113, pp. 414–421. Springer, Heidelberg (2010)
Korzeń, M., Klęsk, P.: Sets of approximating functions with finite Vapnik-Czervonenkis dimension for nearest-neighbors algorithm. Pattern Recognition Letters 32, 1882–1893 (2011)
Moore, A.W., Atkeson, C.G., Schaal, S.A.: Memory-based learning for control. Technical Report CMU-RI-TR-95-18, Carnegie-Mellon University, Robotics Institute (1995)
Piegat, A., Wąsikowska, B., Korzeń, M.: Application of the self-learning, 3-point mini-model for modeling of unemployment rate in Poland. Studia Informatica, University of Szczecin (2010) (in Polish)
Piegat, A., Wąsikowska, B., Korzeń, M.: Differences between the method of mini-models and the k-nearest neighbors on example of modeling of unemployment rate in Poland. In: Proceedings of 9th Conference on Information Systems in Management, pp. 34–43. WULS Press, Warsaw (2011)
Plucinski, M.: Application of data with missing attributes in the probability RBF neural network learning and classification. In: Sołdek, J., Drobiazgiewicz, L. (eds.) Artificial Intelligence and Security in Computing Systems: 9th International Conference ACS 2002: Proceedings, pp. 63–72. Kluwer Academic Publishers, Boston (2003)
Wasserman, P.D.: Advanced methods in neural computing. Van Nostrand Reinhold, New York (1993)
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Pluciński, M. (2012). Mini-models – Local Regression Models for the Function Approximation Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_19
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DOI: https://doi.org/10.1007/978-3-642-29350-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
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