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ORCIDECML 1993: Vienna, Austria
- Pavel Brazdil:
Machine Learning: ECML-93, European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993, Proceedings. Lecture Notes in Computer Science 667, Springer 1993, ISBN 3-540-56602-3
Invited Papers
- J. Ross Quinlan, R. Mike Cameron-Jones:
FOIL: A Midterm Report. 3-20 - Stephen H. Muggleton:
Inductive Logic Programming: Derivations, Successes and Shortcomings. 21-37
Research Papers
Inductive Logic Programming
- Irene Stahl, Birgit Tausend, Rüdiger Wirth:
Two Methods for Improving Inductive Logic Programming Systems. 41-55 - Peter Idestam-Almquist:
Generalization under Implication by using Or-Introduction. 56-64 - Stefan Wrobel:
On the Proper Definition of Minimality in Specialization and Theory Revision. 65-82 - Peter A. Flach:
Predicate Invention in Inductive Data Engineering. 83-94 - Patrick R. J. van der Laag, Shan-Hwei Nienhuys-Cheng:
Subsumption and Refinement in Model Inference. 95-114 - Jörg-Uwe Kietz:
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming. 115-123 - Henrik Boström:
Improving Example-Guided Unfolding. 124-135
Probabilistic Approaches to Learning
- James Cussens
:
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability. 136-152 - Pat Langley:
Induction of Recursive Bayesian Classifiers. 153-164
Inductive Learning
- Floriana Esposito, Donato Malerba, Giovanni Semeraro:
Decision Tree Pruning as a Search in the State Space. 165-184 - Luís Torgo:
Controlled Redundancy in Incremental Rule Learning. 185-195 - Antoine Cornuéjols:
Getting Order Independence in Incremental Learning. 196-212 - Maciej Modrzejewski:
Feature Selection Using Rough Sets Theory. 213-226
Learning in Dynamic Environments
- Gerhard Widmer, Miroslav Kubat:
Effective Learning in Dynamic Environments by Explicit Context Tracking. 227-243 - Fredrik Kilander, Carl Gustaf Jansson:
COBBIT - A Control Procedure for COBWEB in the Presence of Concept Drift. 244-261
Genetic Algorithms
- Steffen Schulze-Kremer:
Genetic Algorithms for Protein Tertiary Structure Prediction. 262-279 - Gilles Venturini:
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts. 280-296 - Pierre Brézellec, Henry Soldano:
SAMIA: A Bottom-Up Learning Method Using a Simulated Annealing Algorithm. 297-312
Position Papers
Inductive Logic Programming
- Irene Stahl:
Predicate Invention in ILP - an Overview. 313-322 - Francesco Bergadano, Daniele Gunetti:
Funtional Inductive Logic Programming with Queries to the User. 323-328 - Tim Niblett:
A Note on Refinement Operators. 329-335 - Masami Hagiya:
An Iterative and Bottom-up Procedure for Proving-by-Example. 336-341
Learnability
- Saso Dzeroski, Stephen H. Muggleton, Stuart Russell:
Learnability of Constrained Logic Programs. 342-347 - Shan-Hwei Nienhuys-Cheng, Mark Polman:
Complexity Dimensions and Learnability. 348-353 - Tibor Hegedüs:
Can Complexity Theory Benefit from Learning Theory? 354-359
Learning from Time Dependent Data
- Peter Clark, Stan Matwin:
Learning Domain Theories using Abstract Beckground Knowledge. 360-365 - Miroslav Kubat, Doris Flotzinger, Gert Pfurtscheller:
Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods. 366-371 - Charles X. Ling, Ralph Buchal:
Learning to Control Dynamic Systems with Automatic Quantization. 372-377
Inductive Learning and Applications
- Dieter Fensel, Markus Wiese:
Refinement of Rule Sets with JoJo. 378-383 - Luís Torgo:
Rule Combination in Inductive Learning. 384-389 - Günter Seidelmann:
Using Heuristics to Speed up Induction on Continuous-Valued Attributes. 390-395 - Jean-Gabriel Ganascia, Jérôme Thomas, Philippe Laublet:
Integrating Models of Knowledge and Machine Learning. 396-401 - Peter D. Turney:
Exploiting Context When Learning to Classify. 402-407 - Lena Gaga, Vassilis Moustakis, Giorgos Charissis, Stelios C. Orphanoudakis:
IDDD: An Inductive, Domain Dependent Decision Algorithm. 408-413 - José Luís Ferreira, Joaquim Correia, Thomas Jamet, Ernesto Costa:
An Application of Machine Learning in the Domain of Loan Analysis. 414-419
Neural Network Learning
- Raqui Kane, Irina Tchoumatchenko, Maurice Milgram:
Extraction of Knowledge from Data Using Constrained Neural Networks. 420-425
Workshop and Panel Overview Papers
- Enric Plaza, Agnar Aamodt, Ashwin Ram, Walter Van de Velde, Maarten van Someren:
Integrated Learning Architectures. 429-441 - William M. Spears, Kenneth A. De Jong, Thomas Bäck, David B. Fogel, Hugo de Garis:
An Overview of Evolutionary Computation. 442-459 - Pieter W. Adriaans:
ML Techniques and Text Analysis. 460-469
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