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  • Conference proceedings
  • © 2019

Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11052)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Conference series link(s): ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Conference proceedings info: ECML PKDD 2018.

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  • ISBN: 978-3-030-10928-8
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Table of contents (50 papers)

  1. Front Matter

    Pages I-XXX
  2. Graphs

    1. Front Matter

      Pages 1-1
    2. Temporally Evolving Community Detection and Prediction in Content-Centric Networks

      • Ana Paula Appel, Renato L. F. Cunha, Charu C. Aggarwal, Marcela Megumi Terakado
      Pages 3-18
    3. Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

      • Carl Yang, Mengxiong Liu, Frank He, Xikun Zhang, Jian Peng, Jiawei Han
      Pages 37-54
    4. Risk-Averse Matchings over Uncertain Graph Databases

      • Charalampos E. Tsourakakis, Shreyas Sekar, Johnson Lam, Liu Yang
      Pages 71-87
    5. Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks

      • Wangsu Hu, Zijun Yao, Sen Yang, Shuhong Chen, Peter J. Jin
      Pages 88-104
    6. Social-Affiliation Networks: Patterns and the SOAR Model

      • Dhivya Eswaran, Reihaneh Rabbany, Artur W. Dubrawski, Christos Faloutsos
      Pages 105-121
    7. ONE-M: Modeling the Co-evolution of Opinions and Network Connections

      • Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski et al.
      Pages 122-140
    8. Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletions

      • Kijung Shin, Jisu Kim, Bryan Hooi, Christos Faloutsos
      Pages 141-157
    9. Semi-supervised Blockmodelling with Pairwise Guidance

      • Mohadeseh Ganji, Jeffrey Chan, Peter J. Stuckey, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao et al.
      Pages 158-174
  3. Kernel Methods

    1. Front Matter

      Pages 175-175
    2. Large-Scale Nonlinear Variable Selection via Kernel Random Features

      • Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet
      Pages 177-192
    3. Fast and Provably Effective Multi-view Classification with Landmark-Based SVM

      • Valentina Zantedeschi, Rémi Emonet, Marc Sebban
      Pages 193-208
  4. Learning Paradigms

    1. Front Matter

      Pages 225-225
    2. VC-Dimension Based Generalization Bounds for Relational Learning

      • Ondřej Kuželka, Yuyi Wang, Steven Schockaert
      Pages 259-275

Other Volumes

  1. Machine Learning and Knowledge Discovery in Databases

    European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
  2. Machine Learning and Knowledge Discovery in Databases

    European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II
  3. Machine Learning and Knowledge Discovery in Databases

    European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III
  4. ECML PKDD 2018 Workshops

    Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings
  5. ECML PKDD 2018 Workshops

    DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers

About this book

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.

Keywords

  • artificial intelligence
  • bayesian networks
  • big data
  • classification
  • clustering
  • data mining
  • data security
  • image processing
  • learning algorithms
  • machine learning
  • neural networks
  • recommender systems
  • semantics
  • signal filtering and prediction
  • signal processing
  • social networking
  • social networks
  • supervised learning
  • Support Vector Machines (SVM)

Editors and Affiliations

  • IBM Research - Ireland, Dublin, Ireland

    Michele Berlingerio

  • Institute for Scientific Interchange, Turin, Italy

    Francesco Bonchi

  • University of Nottingham, Nottingham, UK

    Thomas Gärtner

  • University College Dublin, Dublin, Ireland

    Neil Hurley, Georgiana Ifrim

Bibliographic Information

Buying options

eBook USD 99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-10928-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 129.99
Price excludes VAT (USA)