Buch, Englisch, 320 Seiten, Book, Gewicht: 520 g
International Workshop (TANC'97) Hong Kong, 26-28 May 1997
Buch, Englisch, 320 Seiten, Book, Gewicht: 520 g
ISBN: 978-981-3083-70-7
Verlag: Springer
Over the past decade or so, neural computation has emerged as a research area with active involvement by researchers from a number of different disciplines, including computer science, engineering, mathematics, neurobiology, physics, and statistics. The workshop brought together researchers with a diverse background to review the current status of neural computation research. Three aspects of neural computation have been emphasized: neuroscience aspects, computational and Mathematical aspects, and statistical physics aspects. This book contains 28 contributions from frontier researchers in these fields. Thoroughly re-edited, and in some cases revised post-workshop, these papers collated into this review volume provide a top-class reference summary of the state-of-the-art work done in this field.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
- Mathematik | Informatik EDV | Informatik Technische Informatik Systemverwaltung & Management
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Computeralgebra
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
Weitere Infos & Material
SOME PAPERS PRESENTED: The Natural Gradient Learning Algorithm for Neural Networks (Shun-ichi Amari); Optimal Bayesian Online Learning (Ole Winther & Sara A. Solla), Experts or an Ensemble? A Statistical Mechanics Perspective of Multiple Neural Network Approaches (Jong-Hoon Oh & Kukjin Kang); Stochastic Orientation of the Generating Distribution in Very Fast Simulated Reannealing (Bruce E. Rosen); Primary Cortical Dynamics for Visual Grouping (Zhaoping Li); Information Merging in Neural Modelling (Massimo Battisti et al); The Dynamics of On-Line Learning (Sara A. Solla); A Simple Perceptron that Learns Non-Monotonic Rules (Jun-ichi INOUE et al); Learning Continuous Attractors in Recurrent Networks (H. Sebastian Seung); etc.
