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Integrating Models of Discrimination and Characterization for Learning from Examples in Open Domains
Responsible organisation
1997 (English)Conference paper, (Refereed) Published
Abstract [en]

It is argued that in applications of concept learning from examples where not every possible category of the domain is present in the training set (i.e., most real world applications), classification performance can be improved by integrating suitable discriminative and characteristic classification schemes. The suggested approach is to first discriminate between the categories present in the training set and then characterize each of these categories against all possible categories. To show the viability of this approach, a number of different discriminators and characterizers are integrated and tested. In particular, a novel characterization method that makes use of the information about the statistical distribution of feature values that can be extracted from the training examples is used. The experimental results strongly supports the thesis of the paper.

Place, publisher, year, edition, pages
Nagoya, Japan: Morgan Kaufmann Publishers , 1997.
Keyword [en]
learning by example, pattern classification
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-9375ISI: 000072707200121Local ID: oai:bth.se:forskinfoF30BDC78265A94DFC12568A3002CAB2CISBN: 1-55860-480-4 (print)OAI: oai:DiVA.org:bth-9375DiVA: diva2:837205
Conference
IJCAI-97 : proceedings of the Fifteenth International Joint Conference on Artificial Intelligence
Available from: 2012-09-18 Created: 2000-03-15 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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