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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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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/621/620515/620515.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/10.1007/978-3-030-10928-8
isbn_softcover978-3-030-10927-1
isbn_ebook978-3-030-10928-8Series 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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Temporally Evolving Community Detection and Prediction in Content-Centric Networksch temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurri
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