✦ LIBER ✦

Improving neural network training solutions using regularisation

✍ Scribed by Seán Mc Loone; George Irwin

Book ID: 114295721
Publisher: Elsevier Science
Year: 2001
Tongue: English
Weight: 286 KB
Volume: 37
Category: Article
ISSN: 0925-2312
DOI: 10.1016/s0925-2312(00)00314-3

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Evolutionary time-frequency distribution

Evolutionary time-frequency distributions using Bayesian regularised neural network model

✍ Shafi, I.; Ahmad, J.; Shah, S.I.; Kashif, F.M. 📂 Article 📅 2007 🏛 The Institution of Engineering and Technology 🌐 English ⚖ 673 KB

Regularisation of neural networks for im

Regularisation of neural networks for improved load forecasting in the power system

✍ Osowski, S.; Siwek, K. 📂 Article 📅 2002 🏛 The Institution of Electrical Engineers 🌐 English ⚖ 548 KB

Improving performance in pulse radar det

Improving performance in pulse radar detection using Bayesian regularization for neural network training

✍ Prasoon Kumar; S.N. Merchant; U.B. Desai 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 160 KB

Training the brain using neural-network

Training the brain using neural-network models

✍ SKOYLES, JOHN R. 📂 Article 📅 1988 🏛 Nature Publishing Group 🌐 English ⚖ 148 KB

Accelerating neural network training usi

Accelerating neural network training using weight extrapolations

✍ S.V. Kamarthi; S. Pittner 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 204 KB

The backpropagation (BP) algorithm for training feedforward neural networks has proven robust even for difficult problems. However, its high performance results are attained at the expense of a long training time to adjust the network parameters, which can be discouraging in many real-world applicat

Sparse solution in training artificial n

Sparse solution in training artificial neural networks

✍ Orazio Giustolisi 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 311 KB