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Relational Learning with Decision Trees (1996)  (Make Corrections)  (12 citations)
Peter Geibel, Fritz Wysotzki
European Conference on Artificial Intelligence



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Abstract: . In this paper, we describe two different learning tasks for relational structures. When learning a classifier for structures, the relational structures in the training sets are classified as a whole. Contrarily, when learning a context dependent classifier for elementary objects, the elementary objects of the relational structures in the training set are classified. We investigate the question how such classifications can be induced automatically from a given training set containing... (Update)

Context of citations to this paper:   More

.... a complex structure often cannot be described as fixed length feature vectors without losing important structural information (see [15, 16, 17]) In machine learning, these objects often are represented in a logical framework (see [1, 26] for an overview) Inductive Logic...

.... complex structure often cannot be described as fixed length feature vectors without losing important structural information (see [13, 14, 15]) In machine learning, these objects often are represented in a logical framework (see [1, 23] for an overview) Inductive Logic...

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Learning with Feature Description Logics - Chad Cumby And (2002)   (Correct)
Competitive Winner-Takes-All Clustering in the Domain of Graphs - Jain, Wysotzki   (Correct)
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8:   Learning relational concepts with decision trees - Geibel, Wysotzki - 1996
7:   Learning Logical Definitions from Relations (context) - Quinlan - 1990
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BibTeX entry:   (Update)

P. Geibel and F. Wysotzki. Relational learning with decision trees. In Proc. Twelfth European Conference on Artificial Intelligence (ECAI-96), pages 428--432, 1996. http://citeseer.ist.psu.edu/geibel96relational.html   More

@inproceedings{ geibel96relational,
    author = "Peter Geibel and Fritz Wysotzki",
    title = "Relational Learning with Decision Trees",
    booktitle = "European Conference on Artificial Intelligence",
    pages = "428-432",
    year = "1996",
    url = "citeseer.ist.psu.edu/geibel96relational.html" }
Citations (may not include all citations):
1359   Induction of Decision Trees (context) - Quinlan - 1986
65   The application of Inductive Logic Programming to finite ele.. - Dolsak, Muggleton - 1992
54   Learning Conjunctive Concepts in Structural Domains (context) - Haussler - 1989
37   Inductive Logic Programming (context) - Lavrac - 1994
24   Applications of a logical discovery engine - Dehaspe, van Laer et al. - 1994
14   Lernfahige Klassifizierungssysteme (context) - Unger, Wysotzki - 1981
10   Knowledge Discovery in a Database of Mutagenic Chemicals (context) - King, Sternberg et al. - 1995
8   number 558 in Lecture Notes in Mathematics (context) - Weisfeiler, Construction et al. - 1976
8   Concept Learning by Structured Examples - An Algebraic Appro.. (context) - Wysotzki, Kolbe et al. - 1981
4   Zum Nachweis der Isomorphie von endlichen Graphen (context) - Tinhofer - 1976
2   Induction of Context DependentConcepts (context) - Geibel, Wysotzki - 1995
2   A graph theoretic approach for representation and classifica.. (context) - Sobik, Sommerfeld - 1982
1   Begriffslernen fur strukturierte Objekte (Concept Learning f.. (context) - Geibel, Schadler et al. - 1995



The graph only includes citing articles where the year of publication is known.


Documents on the same site (http://satie.cs.tu-berlin.de/~geibel/publications.html):   More
Learning Context Dependent Concepts - Geibel, Wysotzki (1995)   (Correct)
A Logical Framework for Graph Theoretical Decision Tree Learning - Geibel, Wysotzki (1997)   (Correct)
Induction Of Relational Decision Trees By Optimization Of.. - Geibel, Wysotzki (1997)   (Correct)

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