9. ECML 1997: Prague, Czech Republic

Invited Papers

• Stuart J. Russell:
  Uncertain Learning Agents (Abstract). 3
  
• Luc Stells:
  Constructing and Sharing Perceptual Distiinctions. 4-13
  
• Paul M. B. Vitányi, Ming Li:
  On Prediction by Data Compression. 14-30
  

Regular Papers

• Eva Armengol, Enric Plaza:
  Induction of Feature Terms With INDIE. 33-48
  
• Cristina Baroglio:
  Exploiting Qualitative Knoledge to Enhance Skill Acquisition. 49-56
  
• Pawel Cichosz:
  Integrated Learning and Planning Based on Truncating Temporal Differences. 57-72
  
• Luc De Raedt, Peter Idestam-Almquist, Gunther Sablon:
  Theta-Subsumption for Structural Matching. 73-84
  
• Gülsen Demiröz, H. Altay Güvenir:
  Classification by Voting Feature Intervals. 85-92
  
• Janez Demsar, Blaz Zupan, Marko Bohanec, Ivan Bratko:
  Constructing Intermediate Concepts by Decomposition of Real Functions. 93-107
  
• Dragan Gamberger, Nada Lavrac:
  Conditions for Occam's Razor Applicability and Noise Elimination. 108-123
  
• Yuh-Jyh Hu:
  Learning Different Types of New Attributes by Combining the Neural Network and Iterative Attribute Construction. 124-137
  
• Alan Hutchinson:
  Metrics on Terms and Clauses. 138-145
  
• Miroslav Kubat, Robert C. Holte, Stan Matwin:
  Learning When Negative Examples Abound. 146-153
  
• Nicolas Lachiche, Pierre Marquis:
  A Model for Generalization Based on Confirmatory Induction. 154-161
  
• Lionel Martin, Christel Vrain:
  Learning Linear Constraints in Inductive Logic Programming. 162-169
  
• Rémi Munos:
  Finite-Element Methods with Local Triangulation Refinement for Continuous Reimforcement Learning Problems. 170-182
  
• Nikolay I. Nikolaev, Vanio Slavov:
  Inductive Genetic Programming with Decision Trees. 183-190
  
• Tim Oates, Matthew D. Schmill, Paul R. Cohen:
  Parallel and Distributed Search for Structure in Multivariate Time Series. 191-198
  
• Bernhard Pfahringer:
  Compression-Based Pruning of Decision Lists. 199-212
  
• Rafal Salustowicz, Jürgen Schmidhuber:
  Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space. 213-220
  
• Rudy Setiono, Huan Liu:
  NeuroLinear: A System for Extracting Oblique Decision Rules from Neural Networks. 221-233
  
• Ning Shan, Howard J. Hamilton, Nick Cercone:
  Inducing and Using Decision Rules in the GRG Knowledge Discovery System. 234-241
  
• Csaba Szepesvári:
  Learning and Exploitation Do Not Conflict Under Minimax Optimality. 242-249
  
• Kai Ming Ting, Boon Toh Low:
  Model Combination in the Multiple-Data-Batches Scenario. 250-265
  
• Luís Torgo, João Gama:
  Search-Based Class Discretization. 266-273
  
• Fabien Torre, Céline Rouveirol:
  Natural Ideal Operators in Inductive Logic Programming. 274-289
  
• Koen Vanhoof, Josée Bloemer, K. Pauwels:
  A Case Study in Loyality and Satisfaction Research. 290-297
  
• Cristina Versino, Luca Maria Gambardella:
  Ibots Learn Genuine Team Solutions. 298-311
  
• Ricardo Vilalta, Gunnar Blix, Larry A. Rendell:
  Global Data Analysis and the Fragmentation Problem in Decision Tree Induction. 312-326
  

Workshop Position Papers

• David W. Aha, Dietrich Wettschereck:
  Case-Based Learning: Beyond Classification of Feature Vectors. 329-336
  
• Walter Daelemans, Antal van den Bosch, Ton Weijters:
  Empirical Learning of Natural Language Processing Task. 337-344
  
• Michael Kaiser, Volker Klingspor, Holger Friedrich:
  Human-Agent Interaction and Machine Learning. 345-352
  
• Charles Taylor, Gholamreza Nakhaeizadeh:
  Learning in Dynamically Changing Domains: Theory Revision and Context Dependence Issues. 353-360