ECML 1993: Vienna, Austria

Invited Papers

• J. Ross Quinlan, R. Mike Cameron-Jones:
  FOIL: A Midterm Report. 3-20
  
• Stephen Muggleton:
  Inductive Logic Programming: Derivations, Successes and Shortcomings. 21-37
  

Research Papers

Inductive Logic Programming

• Irene Stahl, Birgit Tausend, Rüdiger Wirth:
  Two Methods for Improving Inductive Logic Programming Systems. 41-55
  
• Peter Idestam-Almquist:
  Generalization under Implication by using Or-Introduction. 56-64
  
• Stefan Wrobel:
  On the Proper Definition of Minimality in Specialization and Theory Revision. 65-82
  
• Peter A. Flach:
  Predicate Invention in Inductive Data Engineering. 83-94
  
• Patrick R. J. van der Laag, Shan-Hwei Nienhuys-Cheng:
  Subsumption and Refinement in Model Inference. 95-114
  
• Jörg-Uwe Kietz:
  Some Lower Bounds for the Computational Complexity of Inductive Logic Programming. 115-123
  
• Henrik Boström:
  Improving Example-Guided Unfolding. 124-135
  

Probabilistic Approaches to Learning

• James Cussens:
  Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability. 136-152
  
• Pat Langley:
  Induction of Recursive Bayesian Classifiers. 153-164
  

Inductive Learning

• Floriana Esposito, Donato Malerba, Giovanni Semeraro:
  Decision Tree Pruning as a Search in the State Space. 165-184
  
• Luís Torgo:
  Controlled Redundancy in Incremental Rule Learning. 185-195
  
• Antoine Cornuéjols:
  Getting Order Independence in Incremental Learning. 196-212
  
• Maciej Modrzejewski:
  Feature Selection Using Rough Sets Theory. 213-226
  

Learning in Dynamic Environments

• Gerhard Widmer, Miroslav Kubat:
  Effective Learning in Dynamic Environments by Explicit Context Tracking. 227-243
  
• Fredrik Kilander, Carl Gustaf Jansson:
  COBBIT - A Control Procedure for COBWEB in the Presence of Concept Drift. 244-261
  

Genetic Algorithms

• Steffen Schulze-Kremer:
  Genetic Algorithms for Protein Tertiary Structure Prediction. 262-279
  
• Gilles Venturini:
  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts. 280-296
  
• Pierre Brézellec, Henry Soldano:
  SAMIA: A Bottom-Up Learning Method Using a Simulated Annealing Algorithm. 297-312
  

Position Papers

Inductive Logic Programming

• Irene Stahl:
  Predicate Invention in ILP - an Overview. 313-322
  
• Francesco Bergadano, Daniele Gunetti:
  Funtional Inductive Logic Programming with Queries to the User. 323-328
  
• Tim Niblett:
  A Note on Refinement Operators. 329-335
  
• Masami Hagiya:
  An Iterative and Bottom-up Procedure for Proving-by-Example. 336-341
  

Learnability

• Saso Dzeroski, Stephen Muggleton, Stuart J. Russell:
  Learnability of Constrained Logic Programs. 342-347
  
• Shan-Hwei Nienhuys-Cheng, Mark Polman:
  Complexity Dimensions and Learnability. 348-353
  
• Tibor Hegedüs:
  Can Complexity Theory Benefit from Learning Theory? 354-359
  

Learning from Time Dependent Data

• Peter Clark, Stan Matwin:
  Learning Domain Theories using Abstract Beckground Knowledge. 360-365
  
• Miroslav Kubat, Doris Flotzinger, Gert Pfurtscheller:
  Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods. 366-371
  
• Charles X. Ling, Ralph Buchal:
  Learning to Control Dynamic Systems with Automatic Quantization. 372-377
  

Inductive Learning and Applications

• Dieter Fensel, Markus Wiese:
  Refinement of Rule Sets with JoJo. 378-383
  
• Luís Torgo:
  Rule Combination in Inductive Learning. 384-389
  
• Günter Seidelmann:
  Using Heuristics to Speed up Induction on Continuous-Valued Attributes. 390-395
  
• Jean-Gabriel Ganascia, Jérôme Thomas, Philippe Laublet:
  Integrating Models of Knowledge and Machine Learning. 396-401
  
• Peter D. Turney:
  Exploiting Context When Learning to Classify. 402-407
  
• Lena Gaga, Vassilis Moustakis, Giorgos Charissis, Stelios C. Orphanoudakis:
  IDDD: An Inductive, Domain Dependent Decision Algorithm. 408-413
  
• José Luís Ferreira, Joaquim Correia, Thomas Jamet, Ernesto Costa:
  An Application of Machine Learning in the Domain of Loan Analysis. 414-419
  

Neural Network Learning

• Raqui Kane, Irina Tchoumatchenko, Maurice Milgram:
  Extraction of Knowledge from Data Using Constrained Neural Networks. 420-425
  

Workshop and Panel Overview Papers

• Enric Plaza, Agnar Aamodt, Ashwin Ram, Walter Van de Velde, Maarten van Someren:
  Integrated Learning Architectures. 429-441
  
• William M. Spears, Kenneth A. De Jong, Thomas Bäck, David B. Fogel, Hugo de Garis:
  An Overview of Evolutionary Computation. 442-459
  
• Pieter W. Adriaans:
  ML Techniques and Text Analysis. 460-469