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ORCIDKnowledge Representation and Organization in Machine Learning 1987: Schloß Ehringerfeld, Germany
- Katharina Morik:
Knowledge Representation and Organization in Machine Learning [Workshop, 1987, Schloß Ehringerfeld, Germany]. Lecture Notes in Computer Science 347, Springer 1989, ISBN 3-540-50768-X - William R. Swartout
, Stephen W. Smoliar:
Explanation: A Source of Guidance for Knowledge Representation. 1-16 - Walter Van de Velde:
(Re)Presentation Issues in Second Generation Expert Systems. 17-49 - Michael Mohnhaupt, Bernd Neumann:
Some Aspects of Learning and Reorganization in an Analogical Representation. 50-64 - Roy Rada, Hafedh Mili:
A Knowledge-Intensive Learning System for Document Retrieval. 65-87 - David C. Littman:
Constructing Expert Systems as Building Mental Models or Toward a Cognitive Ontology for Expert Systems. 88-106 - Katharina Morik:
Sloppy Modeling. 107-134 - Yves Kodratoff, Gheorghe Tecuci:
The Central Role of Explannations in DISCIPLE. 135-147 - Werner Emde:
An Inference Engine for Representing Multiple Theories. 148-176 - Sabine Thieme:
The Acquisition of Model-Knowledge for a Model-Driven Machine Learning Approach. 177-191 - Maarten van Someren:
Using Attribute Dependencies for Rule Learning. 192-210 - Michel Manago, Jim Blythe:
Learning Disjunctive Concepts. 211-230 - Christel Vrain, Yves Kodratoff:
The Use of Analogy in Incremental SBL. 231-246 - David C. Wilkins:
Knowledge Base Refinement Using Apprenticeship Learning Techniques. 247-257 - Michael J. Pazzani:
Creating High Level Knowledge Structures from Simple Elements. 258-288 - Stefan Wrobel:
Demand-Driven Concept Formation. 289-319
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