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Krzyzak A, Linder T, Lugosi C. Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. IEEE Trans Neural Netw. 1996;7(2):475-87doi: 10.1109/72.485681.
Krzyzak, A., Linder, T., & Lugosi, C. (1996). Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. IEEE transactions on neural networks, 7(2), 475-87. https://doi.org/10.1109/72.485681
Krzyzak, A, et al. "Nonparametric estimation and classification using radial basis function nets and empirical risk minimization." IEEE transactions on neural networks vol. 7,2 (1996): 475-87. doi: https://doi.org/10.1109/72.485681
Krzyzak A, Linder T, Lugosi C. Nonparametric estimation and classification using radial basis function nets and empirical risk minimization. IEEE Trans Neural Netw. 1996;7(2):475-87. doi: 10.1109/72.485681. PMID: 18255599.
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IEEE Trans Neural Netw. 1996;7(2):475-87. doi: 10.1109/72.485681.

Nonparametric estimation and classification using radial basis function nets and empirical risk minimization.

IEEE transactions on neural networks

A Krzyzak, T Linder, C Lugosi

Affiliations

  1. Dept. of Comput. Sci., Concordia Univ., Montreal, Que.

PMID: 18255599 DOI: 10.1109/72.485681

Abstract

Studies convergence properties of radial basis function (RBF) networks for a large class of basis functions, and reviews the methods and results related to this topic. The authors obtain the network parameters through empirical risk minimization. The authors show the optimal nets to be consistent in the problem of nonlinear function approximation and in nonparametric classification. For the classification problem the authors consider two approaches: the selection of the RBF classifier via nonlinear function estimation and the direct method of minimizing the empirical error probability. The tools used in the analysis include distribution-free nonasymptotic probability inequalities and covering numbers for classes of functions.

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