Machine learning : ECML-93 : [6th] European Conference on Machine Learning, Vienna, Austria, April 5 7, 1993 : proceedings
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 le...
Salvato in:
| Autore principale: | |
|---|---|
| Ente Autore: | |
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
667 |
| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Machine learning, ECML-93, European Conference on Machine Learning, Vienna, Australia, April 5-7, 1993, proceedings, P.B. Brazdil, ed, Berlin, Springer, 1993, 1 vol. (XII-469 p.), Lecture notes in computer science, 0-387-56602-3 • Machine Learning: ECML-93, Texte imprimé, 9783662186398 |
Sommario:
- 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.