A Neural-Network Approach to Nonparametric and Robust Classification Procedures  

Oleh:

Kurz, L. ; Kowalski, J. M. ; Voudouri-Maniati, E.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 2 (1997), page 288-298.
Topik: robust; neural - network; non parametric; robust classification; procedures

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper algorithms of neural - network type are introduced for solving estimation and classification problems when assumptions about independence, Gaussianity, and stationarity of the observation samples are no longer valid. Specifically, the asymptotic normality of several non parametric classification tests is demonstrated and their implementation using a neural - network approach is presented. Initially, the neural nets train themselves via learning samples for nominal noise and alternative hypotheses distributions resulting in near optimum performance in a particular stochastic environment. In other than the nominal environments, however, high efficiency is maintained by adapting the optimum non linearities to changing conditions during operation via parallel networks, without disturbing the classification process. Furthermore, the superiority in performance of the proposed networks over more traditional neural nets is demonstrated in an application involving pattern recognition.

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