## Machine Learning: ECML-93: European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993. ProceedingsThis volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops. |

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### Contents

A Midterm Report | 3 |

derivations successes and shortcomings | 21 |

Research Papers | 39 |

Two Methods for Improving Inductive Logic Programming Systems | 41 |

Generalization under Implication by using OrIntroduction | 56 |

On the proper definition of minimality in specialization and theory revision | 65 |

Predicate Invention in Inductive Data Engineering | 83 |

Subsumption and Refinement in Model Inference | 95 |

Functional Inductive Logic Programming with Queries to the User | 323 |

A note on refinement operators | 329 |

An Iterative and Bottomup Procedure for ProvingbyExample | 336 |

Learnability of Constrained Logic Programs | 342 |

Complexity Dimensions and Learnability | 348 |

Can Complexity Theory Benefit from Learning Theory? | 354 |

Learning Domain Theories using Abstract Background Knowledge | 360 |

Comparative Study of a Few Methods | 366 |

Some Lower Bounds for the Computational Complexity of Inductive Logic Programming | 115 |

Improving ExampleGuided Unfolding | 124 |

Bayes and PseudoBayes Estimates of Conditional Probabilities and Their Reliability | 136 |

Pat Langley | 153 |

Decision Tree Pruning as a Search in the State Space | 165 |

Controlled Redundancy in Incremental Rule Learning | 185 |

Getting Order Independence in Incremental Learning | 196 |

Feature Selection Using Rough Sets Theory | 213 |

Effective Learning in Dynamic Environments by Explicit Context Tracking | 227 |

COBBITA Control Procedure for COBWEB in the Presence of Concept Drift | 244 |

Genetic Algorithms for Protein Tertiary Structure Prediction | 262 |

a Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts | 280 |

A BOTTOMUP LEARNING METHOD USING A SIMULATED ANNEALING ALGORITHM | 297 |

Position Papers | 311 |

Predicate Invention in ILP an Overview | 313 |

Learning to Control Dynamic Systems with Automatic Quantization | 372 |

Refinement of Rule Sets with JoJo | 378 |

Rule Combination in Inductive Learning | 384 |

Using Heuristics to Speed up Induction on ContinuousValued Attributes | 390 |

Integrating Models of Knowledge and Machine Learning | 396 |

Exploiting Context When Learning to Classify | 402 |

An Inductive Domain Dependent Decision Algorithm | 408 |

An Application of Machine Learning in the Domain of Loan Analysis | 414 |

Extraction of Knowledge from Data Using Constrained Neural Networks | 420 |

Workshop and Panel Overview Papers | 427 |

Integrated Learning Architectures | 429 |

An Overview of Evolutionary Computation | 442 |

ML techniques and text analysis | 460 |

471 | |

### Common terms and phrases

accuracy application approach architecture Artificial Intelligence atoms attributes background knowledge Bayesian classifier clause heads COBBIT COBWEB complexity Computer concept drift constraints context decision tree defined definition dependency described domain theory equivalent error rate estimate evaluation evolutionary algorithms finite FOIL function genetic algorithm given goal heuristic Hornclauses hypothesis ILP systems Inductive Logic Programming input language learnability learner learning algorithm learning system literals Lymphography Machine Learning Michalski minimal Morgan Kaufmann Muggleton mutation negative examples neural network node optimal paper parameters partition performance positive examples possible predicate invention preference bias problem Proceedings proof protein pruning methods Quinlan recursive reduced sentences redundancy refinement operator relation representation restricted revision rules search space selection sequence set of clauses simulated annealing step Stephen Muggleton strategy structure subset subsumes q subsumption task theorem training set tuples variables version space YAILS