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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michele Berlingerio,Francesco Bonchi,Georgiana Ifr Conference p

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發(fā)表于 2025-3-21 19:22:15 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning and Knowledge Discovery in Databases
副標(biāo)題European Conference,
編輯Michele Berlingerio,Francesco Bonchi,Georgiana Ifr
視頻videohttp://file.papertrans.cn/621/620504/620504.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michele Berlingerio,Francesco Bonchi,Georgiana Ifr Conference p
描述The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.?. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.?.The contributions were organized in topical sections named as follows:. Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation.. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning.?. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; bayesian networks; big data; classification; clustering; data mining; data securi
版次1
doihttps://doi.org/10.1007/978-3-030-10925-7
isbn_softcover978-3-030-10924-0
isbn_ebook978-3-030-10925-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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發(fā)表于 2025-3-21 22:58:14 | 只看該作者
Michele Berlingerio,Francesco Bonchi,Georgiana Ifr
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Bryan Hooi,Dhivya Eswaran,Hyun Ah Song,Amritanshu Pandey,Marko Jereminov,Larry Pileggi,Christos Falo
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: Sensor Placement and Anomaly Detection in the Electrical Gridt in our sensor placement algorithm. . Our sensor placement algorithm is provably near-optimal, and both our algorithms outperform existing approaches in accuracy by . or more (F-measure) in experiments. . our algorithms scale ., and our detection algorithm is ., requiring bounded space and constant
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L1-Depth Revisited: A Robust Angle-Based Outlier Factor in High-Dimensional Spaceother proposed angle-based outlier factors on detecting high-dimensional outliers regarding both efficiency and accuracy..In order to avoid the quadratic computational time, we introduce a simple but efficient sampling method named . for estimating L1-depth measure. We also present theoretical analy
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