Machine Learning: ECML-93

FOIL: A midterm report.- Inductive logic programming: Derivations, successes and shortcomings.- Two methods for improving inductive logic programming systems.- Generalization under implication by using or-introduction.- On the proper definition of minimality in specialization and theory revision.- Predicate invention in inductive data engineering.- Subsumption and refinement in model inference.- Some lower bounds for the computational complexity of inductive logic programming.- Improving example-guided unfolding.- Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability.- Induction of recursive Bayesian classifiers.- Decision tree pruning as a search in the state space.- Controlled redundancy in incremental rule learning.- Getting order independence in incremental learning.- Feature selection using rough sets theory.- Effective learning in dynamic environments by explicit context tracking.- COBBIT-A control procedure for COBWEB in the presence of concept drift.- Genetic algorithms for protein tertiary structure prediction.- SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts.- SAMIA: A bottom-up learning method using a simulated annealing algorithm.- Predicate invention in ILP - an overview.- Functional inductive logic programming with queries to the user.- A note on refinement operators.- An iterative and bottom-up procedure for proving-by-example.- Learnability of constrained logic programs.- Complexity dimensions and learnability.- Can complexity theory benefit from Learning Theory?.- Learning domain theories using abstract background knowledge.- Discovering patterns in EEG-signals: Comparative study of a few methods.- Learning to control dynamic systems with automatic quantization.- Refinement of rule sets with JoJo.- Rule combination in inductive learning.- Using heuristics to speed up induction on continuous-valued attributes.- Integrating models of knowledge and Machine Learning.-