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Titlebook: Discovery Science; 18th International C Nathalie Japkowicz,Stan Matwin Conference proceedings 2015 Springer International Publishing Switze

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發(fā)表于 2025-3-21 18:02:56 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Discovery Science
副標(biāo)題18th International C
編輯Nathalie Japkowicz,Stan Matwin
視頻videohttp://file.papertrans.cn/282/281059/281059.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 18th International C Nathalie Japkowicz,Stan Matwin Conference proceedings 2015 Springer International Publishing Switze
描述This book constitutes the proceedings of the 17th InternationalConference on Discovery Science, DS 2015, held in banff, AB, Canada inOctober 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefullyreviewed and selected from 44 submissions.?The combination of recent?advances in the development and analysis of methods for discovering scienti c?knowledge, coming from machine learning, data mining, and intelligent data?analysis, as well as their application in various scienti c domains, on the one?hand, with the algorithmic advances in machine learning theory, on the other?hand, makes every instance of this joint event unique and attractive..
出版日期Conference proceedings 2015
關(guān)鍵詞biomedical knowledge discovery; data mining; evolving data and models; machine learning; Web mining; acti
版次1
doihttps://doi.org/10.1007/978-3-319-24282-8
isbn_softcover978-3-319-24281-1
isbn_ebook978-3-319-24282-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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https://doi.org/10.1007/978-1-349-05134-2be generalized to deal with a large class of string classification problems. The method achieves sensitivity and specificity values of up?to 92?% on the settings we experimented with, while providing intuitive classifiers that are easy to interpret for the domain expert.
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Davide Passaretti,Domenico Vistoccosimilarities within symbols in patterns from a given database based on the definition of patterns we would like to mine, and to use clustering methods based on the similarities computed. Although the original method cannot allow for periods, we generalize it by using the periodicity. We give experim
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