Nonparametric Estimation and Classification Using Radial Basis Function Nets and Empirical Risk Minimization  

Oleh:

Krzyzak, A. ; Linder, T. ; Lugosi, C.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 2 (1996), page 475-487.
Topik: risks; non parametric; estimation; classification; radial basis; risk minimization

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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 non parametric 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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