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Machine learning : ECML-93 : European conference on machine learning, Vienna, Austria, April 5-7, 1993 : proceedings

Author: Pavel B Brazdil
Publisher: Berlin : Springer, 1993.
Series: Lecture notes in computer science, 667.; Lecture notes in computer science., Lecture notes in artificial intelligence.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Congressen (vorm)
Additional Physical Format: Print version:
Machine learning
(NL-LeOCL)105101583
(OCoLC)27726476
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Pavel B Brazdil
ISBN: 9783540475972 3540475974
OCLC Number: 150398454
Notes: Eerdere congressen o.d.t.: European Workingh Sessions on Learning, EWSL.
Description: 1 online resource (xii, 469 pages) : illustrations
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.
Series Title: Lecture notes in computer science, 667.; Lecture notes in computer science., Lecture notes in artificial intelligence.
Responsibility: Pavel B. Brazdil, (ed.).

Abstract:

This volume contains the proceedings of the EurpoeanConference on Machine Learning (ECML-93), continuing thetradition of the five earlier EWSLs (European WorkingSessions on Learning). The  Read more...

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