Neural Nets WIRN VIETRI-97
Subtitle: Proceedings of the 9th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 22-24 May 1997
By: edited by Maria Marinaro, Roberto Tagliaferri
Contributors: Tagliaferri, Roberto
Series: Perspectives in Neural Computing, 1431-6854; Perspectives in neural computing
Publisher: Springer London
Published: 1998
Place of publication: London
Language: eng
Format: [electronic resource] :
Number of pages: 338
ISBN-13: 9781447115205
Description: This volume contains selected papers from WIRN VIETRI-97, the 9th Italian Workshop on Neural Nets, held Vietri sul Mare, Salerno, Italy, from 22-24 May 1997. The papers cover a variety of topics related to neural networks, including pattern recognition, signal processing, theoretical models, applications in science and industry, virtual reality, fuzzy systems, and software algorithms. = By providing the reader with a comprehensive overview of recent research work in this area, the volume makes an invaluab le contribution to the Perspectives in Neural Computing Series. Neural Nets - WIRN VIETRI-97 will provide invaluable reading material for anyone who needs to keep up to date with the latest developments in neural networks and related areas. It will be of particular interest to academic and industrial researchers, and postgraduate and graduate students.
Table of contents: title: A Unifying View of Gradient Calculation and Learning for Locally Recurrent Neural Networks; title: Are Hybrid Fuzzy-Neural Systems Actually Useful in Plasma Engineering?- The GTM as Predictor of Risk in Pregnancy; title: An Application of the Bootstrap 632+ Rule to Ecological Data; title: What Size Needs Testing?- Sequences of Discrete Hopfield Networks for the Maximum Clique Problem; title: Entropy Based Comparison of Neural Networks for Classification; title: Energy Functional and Fixed Points of a Neural Network; title: Latent Variables; title: Speeding up Neural Network Execution; title: Word Recognition by MLP-Based Character Spotting and Dynamic Programming; title: Periodicity Analysis of Unevenly Spaced Data by Means of Neural Networks.