Objective Functions for Training New Hidden Units in Constructive Neural Networks  

Oleh:

Kwok, Tin-Yan ; Yeung, Dit-Yan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 5 (1997), page 1131-1148.
Topik: neural network; objective functions; training; neural networks

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, we study a number of objective functions for training new hidden units in constructive algorithms for multilayer feedforward networks. The aim is to derive a class of objective functions the computation of which and the corresponding weight updates can be done in O (N) time, where N is the number of training patterns. Moreover, even though input weight freezing is applied during the process for computational efficiency, the convergence property of the constructive algorithms using these objective functions is still preserved. We also propose a few computational tricks that can be used to improve the optimization of the objective functions under practical situations. Their relative performance in a set of two - dimensional regression problems is also discussed.

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