𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Grounded Neural Networking: Modeling Complex Quantitative Data

✍ Scribed by Brian Castellani, John Castellani and S. Lee Spray


Book ID
115047304
Publisher
John Wiley and Sons
Year
2003
Tongue
English
Weight
184 KB
Volume
26
Category
Article
ISSN
0195-6086

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Neural Networks for Complex Data
✍ Cottrell, Marie; Olteanu, Madalina; Rossi, Fabrice; Rynkiewicz, Joseph; Villa-Vi πŸ“‚ Article πŸ“… 2012 πŸ› Springer-Verlag 🌐 English βš– 663 KB
Predictive non-linear modeling of comple
✍ Jonas S. Almeida πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 69 KB

An artificial neural network (ANN) is an artificial intelligence tool that identifies arbitrary nonlinear multiparametric discriminant functions directly from experimental data. The use of ANNs has gained increasing popularity for applications where a mechanistic description of the dependency betwee

Bayesian Neural Network Models for Censo
✍ David Faraggi; R. Simon; E. Yaskil; A. Kramar πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 758 KB

Neural networks are considered by many to be very promising tools for classification and prediction. The flexibility of the neural network models often result in over-fit. Shrinking the parameters using a penalized likelihood is often used in order to overcome such over-fit. In this paper we extend

PVT data analysis using neural network m
✍ Normandin, Andre; Grandjean, Bernard P. A.; Thibault, Jules πŸ“‚ Article πŸ“… 1993 πŸ› American Chemical Society 🌐 English βš– 659 KB
Complex networks: From data to models
✍ GonzΓ‘lez, Marta C; BarabΓ‘si, Albert-LΓ‘szlΓ³ πŸ“‚ Article πŸ“… 2007 πŸ› Nature Publishing Group 🌐 English βš– 389 KB