Learning in Certainty-Factor-Based Multilayer Neural Networks for Classification  

Oleh:

Fu, LiMin

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 1 (1998), page 151-158.
Topik: CLASSIFICATION; learning; certainty - factor - based; multilayer; neural network; classification

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The computational framework of rule - based neural networks inherits from the neural network and the inference engine of an expert system. In one approach, the network activation function is based on the certainty factor (CF) model of MYCIN - like systems. In this paper, it is shown theoretically that the neural network using the CF - based activation function requires relatively small sample sizes for correct generalization. This result is also confirmed by empirical studies in several independent domains.

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