Machine Learning: ECML-93: European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993. Proceedings / Edition 1

Machine Learning: ECML-93: European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993. Proceedings / Edition 1

by Pavel B. Brazdil
Pub. Date:
Springer Berlin Heidelberg

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Machine Learning: ECML-93: European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993. Proceedings / Edition 1

This 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.

Product Details

ISBN-13: 9783540566021
Publisher: Springer Berlin Heidelberg
Publication date: 04/23/1993
Series: Lecture Notes in Computer Science Series , #667
Edition description: 1993
Pages: 480
Product dimensions: 6.14(w) x 9.21(h) x 0.36(d)

Table of Contents

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.- Exploiting context when learning to classify.- IDDD: An inductive, domain dependent decision algorithm.- An application of machine learning in the domain of loan analysis.- Extraction of knowledge from data using constrained neural networks.- Integrated learning architectures.- An overview of evolutionary computation.- ML techniques and text analysis.

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