2 Search Results for "Dietterich, Tom"


Document
05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)


Abstract
From 30.01.05 to 04.02.05, the Dagstuhl Seminar 05051 ``Probabilistic, Logical and Relational Learning - Towards a Synthesis'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. 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.

Cite as

Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


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@InProceedings{deraedt_et_al:DagSemProc.05051.1,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Abstracts Collection – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--27},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.1},
  URN =		{urn:nbn:de:0030-drops-4303},
  doi =		{10.4230/DagSemProc.05051.1},
  annote =	{Keywords: Statistical relational learning, probabilistic logic learning, inductive logic programming, knowledge representation, machine learning, uncertainty in artificial intelligence}
}
Document
05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis

Authors: Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)


Abstract
A short report on the Dagstuhl seminar on Probabilistic, Logical and Relational Learning – Towards a Synthesis is given.

Cite as

Luc De Raedt, Tom Dietterich, Lise Getoor, and Stephen H. Muggleton. 05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)


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@InProceedings{deraedt_et_al:DagSemProc.05051.2,
  author =	{De Raedt, Luc and Dietterich, Tom and Getoor, Lise and Muggleton, Stephen H.},
  title =	{{05051 Executive Summary – Probabilistic, Logical and Relational Learning - Towards a Synthesis}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.2},
  URN =		{urn:nbn:de:0030-drops-4121},
  doi =		{10.4230/DagSemProc.05051.2},
  annote =	{Keywords: Reasoning about Uncertainty, Relational and Logical Represenations, Statistical Relational Learning, Inductive Lgoic Programmign}
}
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