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

Front Cover
Springer Science & Business Media, Mar 23, 1993 - Computers - 469 pages
0 Reviews
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.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

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
Authors Index
471
Copyright

Common terms and phrases