A Dynamical System Perspective of Structural Learning With Forgetting  

Oleh:

Miller, D. A. ; Zurada, Jacek M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 9 no. 3 (1998), page 508-515.
Topik: LEARNING; dynamical system; structural learning; forgetting

Ketersediaan

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

Lihat Detail Induk

Isi artikel

Structural learning with forgetting is an established method of using Laplace regularization to generate skeletal artificial neural networks. We develop a continuous dynamical system model of regularization in which the associated regularization parameter is generalized to be a time - varying function. Analytic results are obtained for a Laplace regularizer and a quadratic error surface by solving a different linear system in each region of the weight space. This model also enables a comparison of Laplace and Gaussian regularization. Both of these regularizers have a greater effect in weight space directions which are less important for minimization of a quadratic error function. However, for the Gaussian regularizer, the regularization parameter modifies the associated linear system eigenvalues, in contrast to its function as a control input in the Laplace case. This difference provides additional evidence for the superiority of the Laplace over the Gaussian regularizer.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali