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pro vyhledávání: '"Oliver R. Sampson"'
Publikováno v:
Advances in Intelligent Data Analysis XVII ISBN: 9783030017675
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Widening is a method where parallel resources are used to find better solutions from greedy algorithms instead of merely trying to find the same solutions more quickly. To date, every example of Widening has used some form of communication between th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b854a23ca8a5e1f8c30f64348cd47a0
Autor:
Oliver R. Sampson, Michael R. Berthold
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319463483
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We demonstrate the application of Widening to learning performant Bayesian Networks for use as classifiers. Widening is a framework for utilizing parallel resources and diversity to find models in a hypothesis space that are potentially better than t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4e1b348c9f4fe7fe6fbddbb48b40571
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319463483
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This paper demonstrates an approach in data analysis to minimize overall maintenance costs for the air pressure system of Scania trucks. Feature creation on histograms was used. Randomly chosen subsets of attributes were then evaluated to generate an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::021fe3d687cae9eb566df3bf9aa833f2
https://doi.org/10.1007/978-3-319-46349-0_36
https://doi.org/10.1007/978-3-319-46349-0_36
Autor:
Michael R. Berthold, Oliver R. Sampson
Publikováno v:
Advances in Intelligent Data Analysis XIII ISBN: 9783319125701
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We demonstrate that the previously introduced Widening framework is applicable to state-of-the-art Machine Learning algorithms. Using Krimp, an itemset mining algorithm, we show that parallelizing the search finds better solutions in nearly the same
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34b4ee2bac580f27d0b77a887fff10c9