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dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.date.accessioned2018-09-05T02:58:22Z-
dc.date.available2018-09-05T02:58:22Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-84952684545en_US
dc.identifier.other10.1007/978-3-319-27284-9_7en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952684545&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55603-
dc.description.abstract© Springer International Publishing Switzerland 2016. In many situations, we have an (approximately) linear dependence between several quantities.(Formula presented.) The variance v=σ2of the corresponding approximation error (Formula presented.) often depends on the values of the quantities x1,…,xn: v= v(x1,…,xn); the function describing this dependence is known as the skedactic function. Empirically, two classes of skedactic functions are most successful: multiplicative functions (Formula presented.) and exponential functions (Formula presented.).In this paper, we use natural invariance ideas to provide a possible theoretical explanation for this empirical success; we explain why in some situations multiplicative skedactic functions work better and in some exponential ones. We also come up with a general class of invariant skedactic function that includes both multiplicative and exponential functions as particular cases.en_US
dc.subjectComputer Scienceen_US
dc.titleInvariance explains multiplicative and exponential skedactic functionsen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume622en_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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