Classification Ability of Single Hidden Layer Feedforward Neural Networks  

Oleh:

Babri, H. A. ; Huang, Guang-Bin ; Chen, Yan-Qiu

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 3 (2000), page 799-801.
Topik: CLASSIFICATION; classification; ability; single hidden layer

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Multilayer perceptrons with hard - limiting (signum) activation functions can form complex decision regions. It is well known that a three - layer perceptron (two hidden layers) can form arbitrary disjoint decision regions and a two - layer perceptron (one hidden layer) can form single convex decision regions. This paper further proves that single hidden layer feedforward neural networks (SLFN) with any continuous bounded non constant activation function or any arbitrary bounded (continuous or not continuous) activation function which has unequal limits at infinities (not just perceptrons) can form disjoint decision regions with arbitrary shapes in multidimensional cases, SLFN with some unbounded activation function can also form disjoint decision regions with arbitrary shapes.

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