Machine learning and knowledge disco...
Appice, Annalisa.

 

  • Machine learning and knowledge discovery in databases[electronic resource] :European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015 : proceedings.Part I /
  • 紀錄類型: 書目-語言資料,印刷品 : Monograph/item
    杜威分類號: 006.31
    書名/作者: Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015 : proceedings./ edited by Annalisa Appice ... [et al.].
    其他題名: ECML PKDD 2015
    其他作者: Appice, Annalisa.
    團體作者: Clark Conference
    出版者: Cham : : Springer International Publishing :, 2015.
    面頁冊數: lviii, 709 p. : : ill., digital ;; 24 cm.
    Contained By: Springer eBooks
    標題: Machine learning
    標題: Data mining
    標題: Computer Science.
    標題: Data Mining and Knowledge Discovery.
    標題: Artificial Intelligence (incl. Robotics)
    標題: Pattern Recognition.
    標題: Information Storage and Retrieval.
    標題: Database Management.
    標題: Information Systems Applications (incl. Internet)
    ISBN: 9783319235288
    ISBN: 9783319235271
    摘要、提要註: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
    電子資源: http://dx.doi.org/10.1007/978-3-319-23528-8
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