Learning Capacity and Sample Complexity on Expert Networks  

Oleh:

Fu, LiMin

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 6 (1996), page 1517-1520.
Topik: CAPACITY; learning capacity; sample complexity; networks

Ketersediaan

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

Lihat Detail Induk

Isi artikel

A major development in knowledge - based neural networks is the integration of symbolic expert rule - based knowledge into neural networks, resulting in so - called rule - based neural (or connectionist) networks. An expert network here refers to a particular construct in which the uncertainty management model of symbolic expert systems is mapped into the activation function of the neural network. This paper addresses a yet - to -be - answered question : Why can expert networks generalize more effectively from a finite number of training instances than multilayered perceptrons ? It formally shows that expert networks reduce generalization dimensionality and require relatively small sample sizes for correct generalization.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali