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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150600
Doc. No : ET22392
Main Entry : Will Bridewell
Title Proper : SCIENCE AS AN ANOMALY-DRIVEN ENTERPRISE: A COMPUTATIONAL APPROACH TO GENERATING ACCEPTABLE THEORY REVISIONS IN THE FACE OF ANOMALOUS DATA
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Anomalous data lead to scientific discoveries. Although machine learning systems can beforced to resolve anomalous data, these systems use general learning algorithms to do so. Todetermine whether anomaly-driven approaches to discovery produce more accurate modelsthan the standard approaches, we built a program called Kalpana. We also used Kalpanato explore means for identifying those anomaly resolutions that are acceptable to domainexperts. Our experiments indicated that anomaly-driven approaches can lead to a richer setof model revisions than standard methods. Additionally we identified semantic and syntacticmeasures that are significantly correlated with the acceptability of model revisions. Theseresults suggest that by interpreting data within the context of a model and by interpretingmodel revisions within the context of domain..-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL45601.pdf
Title and statement of responsibility and : SCIENCE AS AN ANOMALY-DRIVEN ENTERPRISE: A COMPUTATIONAL APPROACH TO GENERATING ACCEPTABLE THEORY REVISIONS IN THE FACE OF ANOMALOUS DATA [Thesis]
 
 
 
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