Abstract
Regularization Algorithm (also called Regularization Network) is a technique for solving problems of learning from examples – in particular, the problem of approximating a multivariate function from sparse data. We analyze behavior of Regularization Algorithm for regularizator parameter equal to zero. We propose an approximative version of algorithm in order to overcome the computational cost for large data sets. We give proof of convergence and estimation for error of approximation.
This paper consists of a part of my Master Thesis supervised by A. Skowron.
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Jaworski, W. (2004). A Note on the Regularization Algorithm. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_28
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DOI: https://doi.org/10.1007/978-3-540-25929-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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