Morik, K. & Kietz, J.U. (1989) A Bootstrapping Approach to Conceptual Clustering, in Proc. Sixth Intern.  Home/Search Document Not in Database Summary Related Articles Check |
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Learning Composite Concepts in Description Logics - Larocchia (Correct)
.... title of this section is the title of an article from 1994 [10] In this work the authors, Jorg Uwe Kietz and Katharina Morik, discuss a conceptual clustering method using description logics as framework, that has been concretized by the same authors in 1989 in the machine learning system KLUSTER [15]. KLUSTER is the first attempt to learn concepts represented with description logics. In particular it uses a sublanguage of BACK. As any conceptual clustering method the aim of KLUSTER is to build a concept taxonomy. The formal definition of the problem solved by KLUS 3.1. PREVIOUS ATTEMPTS 43 ....
Morik, K., Kietz, J.-U, "A bootstrapping approach to conceptual clustering ", Proceedings of the Sixth International Workshop on Machine Learning, 1989.
A Polynomial Approach to the - Constructive Induction Of Self-citation (Morik Kietz) (Correct)
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Morik, K. & Kietz, J.U. (1989) A Bootstrapping Approach to Conceptual Clustering, in Proc. Sixth Intern.
MOBAL 2.2 User Guide - Sommer, Emde, Kietz, Morik, Wrobel (1993) Self-citation (Morik Kietz) (Correct)
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Katharina Morik and Jorg-Uwe Kietz. A bootstrapping approach to conceptual clustering. In Proc. Sixth Intern. Workshop on Machine Learning, 1989.
Balanced Cooperative Modeling Using MOBAL - User's Guide - Morik, Sommer (1991) Self-citation (Morik) (Correct)
....machine learning automating some tasks. On the other hand we still want the users to perform their tasks supported by the system. The question is how to organize the cooperation of user and system tools such that both system and user contribute to model building. 1 For further details see [Morik, Jan 1989] and [Morik, 1991] 2 For details on the modeling process see [Morik, Jan 1989] Morik, 1991] MOBAL 1.0 User Guide 4 2 Principles of Cooperation There are different ways to use machine learning algorithms for knowledge acquisition. They correspond to different distributions of work between ....
....users to perform their tasks supported by the system. The question is how to organize the cooperation of user and system tools such that both system and user contribute to model building. 1 For further details see [Morik, Jan 1989] and [Morik, 1991] 2 For details on the modeling process see [Morik, Jan 1989], Morik, 1991] MOBAL 1.0 User Guide 4 2 Principles of Cooperation There are different ways to use machine learning algorithms for knowledge acquisition. They correspond to different distributions of work between system and user. The work share has consequences for the knowledge representation. ....
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Katharina Morik and Jorg-Uwe Kietz. A bootstrapping approach to conceptual clustering. In Proc. Sixth Intern. Workshop on Machine Learning, 1989. MOBAL 1.0 User Guide 32
Overview of Logic-based Learning in Germany - Morik (1998) Self-citation (Morik) (Correct)
....was a collaboration of the Technical University Berlin, the software house Stollmann GmbH, and the Nixdorf Computer AG. The aim of the project was to integrate machine learning into knowledge acquisition 3 . A new paradigm of knowledge acquisition was introduced, the framework of sloppy modeling [Morik, 1989]. We wanted a new type of system that assists a knowledge engineer in building and maintaining a domain model. The user should be in control of how he or she wants to organize the modeling. The modeling process was recognized as an infinite process of enhancing a current model. The user should be ....
....manner, testing instantiations of the more general rule schemata before either instantiating specializations of the schemata or pruning the search. In Germany, several projects work on term subsumption formalisms. The first algorithm that learns such concept definitions from facts is KLUSTER [Morik and Kietz, 1989]. It has been proved that KLUSTER learns in polynomial time [Kietz and Morik, 1993] No further extension of the representation formalism is possible without losing this property. This is an upper bound for logic based learning. Another one has been proved by Muggleton and Feng: it is possible to ....
Morik, K. and Kietz, J.-U. (1989). A bootstrapping approach to conceptual clustering. In Proc. Sixth Intern. Workshop on Machine Learning.
Balanced Cooperative Modeling - Morik (1993) (17 citations) Self-citation (Morik) (Correct)
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Morik, K.& Kietz, J.-U. (1989) A Bootstrapping Approach to Conceptual Clustering. in A. Serge (Ed):Procs. of 6th IWML, San Mateo: Morgan Kaufmann.
Learning Trees and Rules with Set-valued Features - Cohen (1996) (87 citations) (Correct)
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Katharina Morik. A bootstrapping approach to conceptual clustering. In Proceedings of the Sixth International Workshop on Machine Learning, Ithaca, New York, 1989.
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