Learning, Networks and Statistics

1997
ISBN: 978-3-211-82910-3
Verlag: Springer Vienna

Buch, Englisch, Band 382, 230 Seiten, Format (B × H): 170 mm x 244 mm, Gewicht: 422 g

Reihe: CISM International Centre for Mechanical Sciences

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.

Zielgruppe

Research

Autoren/Hrsg.

Della Riccia, Giacomo

Kruse, Rudolf

Lenz, Hans-Joachim

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

Neural Nets: Overtraining in Single-Layer Perceptrons; Neural Networks for Rapid Learning in Computer Vision and Robotics.– Statistics and Networks: Adaptive Market Simulation and Risk Assessment; Processing of Prior-Information in Statistics by Projections on Convex Cones.– Classification and Data Mining: Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps; Data analysis in Industry - A Practical Guideline; Fuzzy Shell Cluster Analysis; Automatic Construction of Decision Trees and Neural Nets for Classification Using Statistical Considerations; From the Art of KDD to the Science of KDD.– Machine Learning: Machine Learning: Between Accuracy and Interpretability; Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: REDUCE; A General Framework for Supporting Relational Concept Learning; Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them.