𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Bayesian selection of important features for feedforward neural networks

✍ Scribed by Kevin L. Priddy; Steven K. Rogers; Dennis W. Ruck; Gregory L. Tarr; Matthew Kabrisky


Book ID
113399405
Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
673 KB
Volume
5
Category
Article
ISSN
0925-2312

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Using feedforward neural networks and fo
✍ Chuen-Lung Chen; David B. Kaber; Patrick G. Dempsey πŸ“‚ Article πŸ“… 2003 πŸ› John Wiley and Sons 🌐 English βš– 829 KB

## Abstract A method was developed to accurately predict the risk of injuries in industrial jobs based on datasets not meeting the assumptions of parametric statistical tools, or being incomplete. Previous research used a backward‐elimination process for feedforward neural network (FNN) input varia

A parallel algorithm for gradient traini
✍ ZdenΔ›k HanzΓ‘lek πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 742 KB

This paper presents a message-passing architecture simulating multilayer neural networks, adjusting its weights for each pair, consisting of an input vector and a desired output vector. First, the multilayer neural network is defined, and the difficulties arising from parallel implementation are cla