10302 Abstracts Collection - Learning paradigms in dynamic environments

From 25.07. to 30.07.2010, the Dagstuhl Seminar 10302 ``Learning paradigms in dynamic environments '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.

[1]  Alex Borgiday.  On the Relative Expressiveness of Description Logics and Predicate Logics , 1996 .

[2]  Pavel Zezula,et al.  Similarity Search: The Metric Space Approach (Advances in Database Systems) , 2005 .

[3]  Shusaku Tsumoto,et al.  An indiscernibility-based clustering method , 2005, 2005 IEEE International Conference on Granular Computing.

[4]  Nicola Fanizzi,et al.  Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases , 2007, CIKM '07.

[5]  James C. Bezdek,et al.  Clustering with a genetically optimized approach , 1999, IEEE Trans. Evol. Comput..

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[8]  Luigi Iannone,et al.  Concept Formation in Expressive Description Logics , 2004, ECML.

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[10]  Katharina Morik,et al.  A Polynomial Approach to the Constructive Induction of Structural Knowledge , 2004, Machine Learning.

[11]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[12]  Pavel Zezula,et al.  Similarity Search - The Metric Space Approach , 2005, Advances in Database Systems.

[13]  James C. Bezdek,et al.  Some new indexes of cluster validity , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Olfa Nasraoui,et al.  One Step Evolutionary Mining of Context Sensitive Associations and Web Navigation Patterns , 2002, SDM.

[15]  Luigi Iannone,et al.  An Algorithm Based on Counterfactuals for Concept Learning in the Semantic Web , 2005, IEA/AIE.

[16]  Alexander Borgida,et al.  Towards Measuring Similarity in Description Logics , 2005, Description Logics.

[17]  Nicola Fanizzi,et al.  Reasoning by Analogy in Description Logics Through Instance-based Learning , 2006, SWAP.

[18]  Nicola Fanizzi,et al.  Induction of Optimal Semi-distances for Individuals based on Feature Sets , 2007, Description Logics.

[19]  D. Fogel,et al.  Discovering patterns in spatial data using evolutionary programming , 1996 .

[20]  Mathias Kirsten,et al.  Relational Distance-Based Clustering , 1998, ILP.

[21]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[22]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[23]  Jens Lehmann.  Concept Learning in Description Logics , 2006 .

[24]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams , 2007, SAC '07.

[25]  Steffen Staab,et al.  Efficient Discovery of Services Specified in Description Logics Languages , 2007, SMRR.

[26]  Jens Lehmann,et al.  A Refinement Operator Based Learning Algorithm for the ALC Description Logic , 2007, ILP.

[27]  Ryszard S. Michalski,et al.  Conceptual Clustering of Structured Objects: A Goal-Oriented Approach , 1986, Artif. Intell..

[28]  Shan-Hwei Nienhuys-Cheng.  Distances and Limits on Herbrand Interpretations , 1998, ILP.

[29]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .