Learning, networks and statistics
Author(s)
Bibliographic Information
Learning, networks and statistics
(CISM courses and lectures, No. 382)
Springer-Verlag, c1997
Available at 7 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Description and Table of Contents
Description
The contents of these proceedings reflect the intention of the organizers of the workshop to bring together scientists and engineers having a strong interest in interdisciplinary work in the fields of computer science, mathematics and applied statistics. Results of this collaboration are illustrated in problems dealing with neural nets, statistics and networks, classification and data mining, and (machine) learning.
Table of Contents
- Neural Nets: S. Raudys: Overtraining in Single-Layer Perceptrons
- H. Ritter: Neural Networks for Rapid Learning in Computer Vision and Robotics.- Statistics and Networks: R.A. Muller: Adaptive Market Simulation and Risk Assessment
- E. Roedel: Processing of Prior-Information in Statistics by Projections on Convex Cones.- Classification and Data Mining: H.H. Bock: Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps
- H. Hellendoorn: Data analysis in Industry - A Practical Guideline
- F. Klawonn, R. Kruse, H. Timm: Fuzzy Shell Cluster Analysis
- F. Wysotzki, W. Muller, B. Schulmeister: Automatic Construction of Decision Trees and Neural Nets for Classification Using Statistical Considerations
- Y. Kodratoff: From the Art of KDD to the Science of KDD.- Machine Learning: I. Bratko: Machine Learning: Between Accuracy and Interpretability
- N. Lavrac, D. Gamberger, P. Turney: Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: REDUCE
- L. Saitta: A General Framework for Supporting Relational Concept Learning
- R. Trappl, J. Furnkranz, J. Petrak, J. Bercovitch: Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them.
by "Nielsen BookData"