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Machine Learning and Knowledge Discovery in Databases

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

  • Michele Berlingerio
  • Francesco Bonchi
  • Thomas Gärtner
  • Neil Hurley
  • Georgiana Ifrim
Conference proceedings ECML PKDD 2018

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11051)

Table of contents

  1. Front Matter
    Pages I-XXXVIII
  2. Adversarial Learning

    1. Front Matter
      Pages 1-1
    2. Lucas Deecke, Robert Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft
      Pages 3-17
    3. Jin-Wen Wu, Fei Yin, Yan-Ming Zhang, Xu-Yao Zhang, Cheng-Lin Liu
      Pages 18-34
    4. Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown
      Pages 35-51
    5. Shang-Tse Chen, Cory Cornelius, Jason Martin, Duen Horng (Polo) Chau
      Pages 52-68
  3. Anomaly and Outlier Detection

    1. Front Matter
      Pages 69-69
    2. Bryan Hooi, Dhivya Eswaran, Hyun Ah Song, Amritanshu Pandey, Marko Jereminov, Larry Pileggi et al.
      Pages 71-86
    3. Shubhranshu Shekhar, Leman Akoglu
      Pages 87-104
    4. Nikhil Gupta, Dhivya Eswaran, Neil Shah, Leman Akoglu, Christos Faloutsos
      Pages 122-138
    5. M. Y. Meghanath, Deepak Pai, Leman Akoglu
      Pages 139-156
    6. Raghavendra Chalapathy, Edward Toth, Sanjay Chawla
      Pages 173-189
  4. Applications

    1. Front Matter
      Pages 191-191
    2. Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek et al.
      Pages 193-208
    3. Silvia Makowski, Lena A. Jäger, Ahmed Abdelwahab, Niels Landwehr, Tobias Scheffer
      Pages 209-225
    4. Omid Mohamad Nezami, Mark Dras, Peter Anderson, Len Hamey
      Pages 226-240
    5. Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
      Pages 241-256
  5. Classification

    1. Front Matter
      Pages 257-257
    2. Tomáš Komárek, Petr Somol
      Pages 259-272
    3. Denis dos Reis, André Maletzke, Everton Cherman, Gustavo Batista
      Pages 273-289
    4. Stijn Decubber, Thomas Mortier, Krzysztof Dembczyński, Willem Waegeman
      Pages 290-305
    5. Rafael Poyiadzi, Raúl Santos-Rodríguez, Tijl De Bie
      Pages 306-321
    6. Luca Masera, Enrico Blanzieri
      Pages 322-336
  6. Clustering and Unsupervised Learning

    1. Front Matter
      Pages 337-337
    2. Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama
      Pages 339-355
    3. Saeed Varasteh Yazdi, Ahlame Douzal-Chouakria, Patrick Gallinari, Manuel Moussallam
      Pages 356-372
    4. Bruno Crémilleux, Arnaud Giacometti, Arnaud Soulet
      Pages 373-389
  7. Deep Learning

    1. Front Matter
      Pages 391-391
    2. Michael Kamp, Linara Adilova, Joachim Sicking, Fabian Hüger, Peter Schlicht, Tim Wirtz et al.
      Pages 393-409
    3. Alex Yuxuan Peng, Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer
      Pages 410-425
    4. Günther Schindler, Matthias Zöhrer, Franz Pernkopf, Holger Fröning
      Pages 426-442
    5. Thomas Lucas, Jakob Verbeek
      Pages 443-458
    6. Guillaume Bono, Jilles Steeve Dibangoye, Laëtitia Matignon, Florian Pereyron, Olivier Simonin
      Pages 459-476
    7. Martin Renqiang Min, Hongyu Guo, Dinghan Shen
      Pages 477-493
    8. Baruch Epstein, Ron Meir, Tomer Michaeli
      Pages 494-509
    9. Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, Latanya Sweeney
      Pages 510-526
    10. Hang Gao, Tim Oates
      Pages 527-540
    11. Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree
      Pages 541-556
    12. Ikechukwu Nkisi-Orji, Nirmalie Wiratunga, Stewart Massie, Kit-Ying Hui, Rachel Heaven
      Pages 557-572
    13. Nanqing Dong, Eric P. Xing
      Pages 573-588
  8. Ensemble Methods

    1. Front Matter
      Pages 589-589
    2. Henry W. J. Reeve, Tingting Mu, Gavin Brown
      Pages 605-619
    3. Vitor Cerqueira, Fabio Pinto, Luis Torgo, Carlos Soares, Nuno Moniz
      Pages 620-636
    4. Jihed Khiari, Luis Moreira-Matias, Ammar Shaker, Bernard Ženko, Sašo Džeroski
      Pages 637-652
  9. Evaluation

    1. Front Matter
      Pages 653-653
    2. Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
      Pages 655-670

Other volumes

  1. DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers
  2. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
  3. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II
  4. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III
  5. Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings

About these proceedings

Introduction

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

  1. 1.IBM Research - IrelandDublinIreland
  2. 2.Institute for Scientific InterchangeTurinItaly
  3. 3.University of NottinghamNottinghamUK
  4. 4.University College DublinDublinIreland
  5. 5.University College DublinDublinIreland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-10925-7
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-10924-0
  • Online ISBN 978-3-030-10925-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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