Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58588
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dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorThongchai Dumrongpokaphanen_US
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorOlga Koshelevaen_US
dc.date.accessioned2018-09-05T04:26:33Z-
dc.date.available2018-09-05T04:26:33Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85037865695en_US
dc.identifier.other10.1007/978-3-319-70942-0_12en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85037865695&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58588-
dc.description.abstract© Springer International Publishing AG 2018. When the amount of data is reasonably small, we can usually fit this data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model’s accuracy. For big data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how to modify the existing machine learning methodology so that it will not only estimate the parameters, but also estimate the accuracy of the resulting model.en_US
dc.subjectComputer Scienceen_US
dc.titleHow to gauge accuracy of processing big data: Teaching machine learning techniques to gauge their own accuracyen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume753en_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsNew Mexico State University Las Crucesen_US
Appears in Collections:CMUL: Journal Articles

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