Learning, Networks and Statistics

1. Front Matter

   Pages ii-viii

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2. Neural Nets

   1. Front Matter

      Pages 1-1

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   2. Overtraining in Single-Layer Perceptrons

      • S. Raudys

      Pages 3-24

   3. Neural Networks for Rapid Learning in Computer Vision and Robotics

      • H. Ritter

      Pages 25-39

3. Statistics and Networks

   1. Front Matter

      Pages 41-41

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   2. Adaptive Market Simulation and Risk Assessment

      • R. A. Müller

      Pages 43-54

   3. Processing of Prior-Information in Statistics by Projections on Convex Cones

      • E. Rödel

      Pages 55-63

4. Classification and Data Mining

   1. Front Matter

      Pages 65-65

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   2. Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps

      • H. H. Bock

      Pages 67-85

   3. Data Analysis in Industry

      • H. Hellendoorn

      Pages 87-104

   4. Fuzzy Shell Cluster Analysis

      • F. Klawonn, R. Kruse, H. Timm

      Pages 105-119

   5. Automatic Construction of Decision Trees and Neural Nets for Classification Using Statistical Considerations

      • F. Wysotzki, W. Müller, B. Schulmeister

      Pages 121-134

   6. From the Art of KDD to the Science of KDD

      • Y. Kodratoff

      Pages 135-160

5. Machine Learning

   1. Front Matter

      Pages 161-161

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   2. Machine Learning: Between Accuracy and Interpretability

      • I. Bratko

      Pages 163-177

   3. Preprocessing by a Cost-Sensitive Literal Reduction Algorithm: Reduce

      • N. Lavrac, D. Gamberger, P. Turney

      Pages 179-196

   4. A General Framework for Supporting Relational Concept Learning

      • L. Saitta

      Pages 197-207

   5. Machine Learning and Case-Based Reasoning: Their Potential Role in Preventing the Outbreak of Wars or in Ending Them

      • R. Trappl, J. Fürnkranz, J. Petrak, J. Bercovitch

      Pages 209-225

6. Back Matter

   Pages 227-230

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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.

• algorithms
• case-based reasoning
• classification
• computer
• computer vision
• data mining
• learning
• machine learning
• mathematics
• networks
• robot
• robotics
• simulation
• statistics
• visualization

• Dip. di Matematica e Informatica, University of Udine, Udine, Italy

  Giacomo Riccia

• Institut für Statistik und Ökonometrie, Free Universität of Berlin, Berlin, Germany

  Hans-Joachim Lenz

• University of Magdeburg, Magdeburg, Germany

  Rudolf Kruse

Policies and ethics