Ultimate Performance of QEM Classifiers  

Oleh:

Comon, P. ; Bienvenu, G.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 6 (1996), page 1535-1537.
Topik: Performance; performance; QEM classifiers

Ketersediaan

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

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Isi artikel

Supervised learning of classifiers often resorts to the minimization of a quadratic error, even if this criterion is more especially matched to non linear regression problems. It is shown that the mapping built by a quadratic error minimization (QEM) tends to output the Bayesian discriminating rules even with non uniform losses, provided the desired responses are chosen accordingly. This property is for instance shared by the multilayer perceptron (MLP). It is shown that their ultimate performance can be assessed with finite learning sets by establishing links with kernel estimators of density.

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