License: 
Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.STACS.2016.47
URN: urn:nbn:de:0030-drops-57483
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2016/5748/
Kötzing, Timo ;
Schirneck, Martin
Towards an Atlas of Computational Learning Theory
Abstract
A major part of our knowledge about Computational Learning stems from comparisons of the learning power of different learning criteria. These comparisons inform about trade-offs between learning restrictions and, more generally, learning settings; furthermore, they inform about what restrictions can be observed without losing learning power.
With this paper we propose that one main focus of future research in Computational Learning should be on a structured approach to determine the relations of different learning criteria. In particular, we propose that, for small sets of learning criteria, all pairwise relations should be determined; these relations can then be easily depicted as a map, a diagram detailing the relations. Once we have maps for many relevant sets of learning criteria, the collection of these maps is an Atlas of Computational Learning Theory, informing at a glance about the landscape of computational learning just as a geographical atlas informs about the earth.
In this paper we work toward this goal by providing three example maps, one pertaining to partially set-driven learning, and two pertaining to strongly monotone learning. These maps can serve as blueprints for future maps of similar base structure.
BibTeX - Entry
@InProceedings{ktzing_et_al:LIPIcs:2016:5748,
author = {Timo K{\"o}tzing and Martin Schirneck},
title = {{Towards an Atlas of Computational Learning Theory}},
booktitle = {33rd Symposium on Theoretical Aspects of Computer Science (STACS 2016)},
pages = {47:1--47:13},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-001-9},
ISSN = {1868-8969},
year = {2016},
volume = {47},
editor = {Nicolas Ollinger and Heribert Vollmer},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2016/5748},
URN = {urn:nbn:de:0030-drops-57483},
doi = {10.4230/LIPIcs.STACS.2016.47},
annote = {Keywords: computational learning, language learning, partially set-driven learning, strongly monotone learning}
}
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Keywords: |
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computational learning, language learning, partially set-driven learning, strongly monotone learning |
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Collection: |
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33rd Symposium on Theoretical Aspects of Computer Science (STACS 2016) |
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Issue Date: |
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2016 |
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Date of publication: |
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16.02.2016 |
