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Computing Upper and...
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Danielson, MatsKTH,Data- och systemvetenskap, DSV,Stockholm University
(author)
Computing Upper and Lower Bounds in Interval Decision Trees
- Article/chapterEnglish2007
Publisher, publication year, extent ...
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Elsevier BV,2007
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printrdacarrier
Numbers
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LIBRIS-ID:oai:DiVA.org:miun-4034
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https://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-4034URI
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https://doi.org/10.1016/j.ejor.2006.06.030DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-37431URI
Supplementary language notes
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Language:English
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Summary in:English
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Classification
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Subject category:ref swepub-contenttype
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Subject category:art swepub-publicationtype
Notes
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VR-Computer Science
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QC 20110812
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This article presents algorithms for computing optima in decision trees with imprecise probabilities and utilities. In tree models involving uncertainty expressed as intervals and/or relations, it is necessary for the evaluation to compute the upper and lower bounds of the expected values. Already in its simplest form, computing a maximum of expectancies leads to quadratic programming (QP) problems. Unfortunately, standard optimization methods based on QP (and BLP - bilinear programming) are too slow for the evaluation of decision trees in computer tools with interactive response times. Needless to say, the problems with computational complexity are even more emphasized in multi-linear programming (MLP) problems arising from multi-level decision trees. Since standard techniques are not particularly useful for these purposes, other, non-standard algorithms must be used. The algorithms presented here enable user interaction in decision tools and are equally applicable to all multi-linear programming problems sharing the same structure as a decision tree.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Ekenberg, LoveKTH,Mittuniversitetet,Institutionen för informationsteknologi och medier (-2013),Data- och systemvetenskap, DSV(Swepub:kth)u1b0ge2m
(author)
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KTHData- och systemvetenskap, DSV
(creator_code:org_t)
Related titles
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In:European Journal of Operational Research: Elsevier BV181:2, s. 808-8160377-22171872-6860
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