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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III

  • Yasemin Altun
  • Kamalika Das
  • Taneli Mielikäinen
  • Donato Malerba
  • Jerzy Stefanowski
  • Jesse Read
  • Marinka Žitnik
  • Michelangelo Ceci
  • Sašo Džeroski

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10536)

Table of contents

  1. Front Matter
    Pages I-XXXV
  2. Applied Data Science Track

    1. Front Matter
      Pages 1-1
    2. Rui Chen, Jiajun Liu
      Pages 3-14
    3. Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman
      Pages 15-26
    4. Cansu Sen, Thomas Hartvigsen, Elke Rundensteiner, Kajal Claypool
      Pages 52-63
    5. You-Luen Lee, Da-Cheng Juan, Xuan-An Tseng, Yu-Ting Chen, Shih-Chieh Chang
      Pages 64-76
    6. Sara Melvin, Wenchao Yu, Peng Ju, Sean Young, Wei Wang
      Pages 89-101
    7. C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso
      Pages 102-113
    8. Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts, Jasper N. Adegeest et al.
      Pages 114-126
    9. Bjarke Felbo, Pål Sundsøy, Alex ‘Sandy’ Pentland, Sune Lehmann, Yves-Alexandre de Montjoye
      Pages 140-152
    10. Arijit Biswas, Mukul Bhutani, Subhajit Sanyal
      Pages 153-165
    11. Dieter Hendricks, Stephen J. Roberts
      Pages 166-178
    12. Joseph Bockhorst, Shi Yu, Luisa Polania, Glenn Fung
      Pages 179-190
    13. Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
      Pages 191-203
    14. Jianing Zhao, Daniel M. Runfola, Peter Kemper
      Pages 204-215
    15. Tathagata Mukherjee, Michael Duckett, Piyush Kumar, Jared Devin Paquet, Daniel Rodriguez, Mallory Haulcomb et al.
      Pages 216-227
    16. Lichao Sun, Yuqi Wang, Bokai Cao, Philip S. Yu, Witawas Srisa-an, Alex D. Leow
      Pages 228-240
    17. Shuhao Wang, Cancheng Liu, Xiang Gao, Hongtao Qu, Wei Xu
      Pages 241-252
    18. Mohammad A. Tayebi, Uwe Glässer, Patricia L. Brantingham, Hamed Yaghoubi Shahir
      Pages 253-265
    19. Yaakov HaCohen-kerner, Ziv Ido, Ronen Ya’akobov
      Pages 266-278
    20. Gianni Barlacchi, Alberto Rossi, Bruno Lepri, Alessandro Moschitti
      Pages 279-291
    21. Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru et al.
      Pages 292-304
    22. Stefan Thaler, Vlado Menkovski, Milan Petkovic
      Pages 305-316
    23. Haytham Assem, Salem Ghariba, Gabor Makrai, Paul Johnston, Laurence Gill, Francesco Pilla
      Pages 317-329
    24. Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica
      Pages 330-342
  3. Nectar Track

    1. Front Matter
      Pages 343-343
    2. Rohit Kumar, Muhammad Aamir Saleem, Toon Calders, Xike Xie, Torben Bach Pedersen
      Pages 345-348
    3. Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica
      Pages 349-353
    4. Florian Lemmerich, Philipp Singer, Martin Becker, Lisette Espin-Noboa, Dimitar Dimitrov, Denis Helic et al.
      Pages 354-357
    5. Fabian Bock, Sergio Di Martino, Monika Sester
      Pages 358-362
    6. Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, Sašo Džeroski
      Pages 363-367
    7. Tetsuro Kitahara
      Pages 368-372
    8. Maria Brbić, Matija Piškorec, Vedrana Vidulin, Anita Kriško, Tomislav Šmuc, Fran Supek
      Pages 373-377
    9. Jovan Tanevski, Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski
      Pages 378-382
    10. Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
      Pages 383-387
    11. Nils M. Kriege, Christopher Morris
      Pages 388-392
  4. Demo Track

    1. Front Matter
      Pages 393-393
    2. Kamalika Das, Ilya Avrekh, Bryan Matthews, Manali Sharma, Nikunj Oza
      Pages 395-399
    3. Uchenna Akujuobi, Xiangliang Zhang
      Pages 400-403
    4. Louis Kirsch, Niklas Riekenbrauck, Daniel Thevessen, Marcus Pappik, Axel Stebner, Julius Kunze et al.
      Pages 404-408
    5. Božidara Cvetković, Martin Gjoreski, Jure Šorn, Pavel Maslov, Mitja Luštrek
      Pages 414-418
    6. Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczȩch
      Pages 419-422
    7. Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev et al.
      Pages 423-427
    8. Yifeng Gao, Qingzhe Li, Xiaosheng Li, Jessica Lin, Huzefa Rangwala
      Pages 428-431
    9. Livio Bioglio, Ruggero G. Pensa, Valentina Rho
      Pages 432-436

Other volumes

  1. European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I
  2. European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II
  3. Machine Learning and Knowledge Discovery in Databases
    European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III

About these proceedings

Introduction

The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. 

The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. 

The contributions were organized in topical sections named as follows:
Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.
Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.
Part III: applied data science track; nectar track; and demo track.

Keywords

anomaly detection artificial intelligence Bayesian networks classification clustering algorithms data mining data security data stream image processing Kernel method learning algorithms machine learning neural networks recommender systems reinforcement learning signal processing social networking supervised learning support vector machines (SVM)

Editors and affiliations

  • Yasemin Altun
    • 1
  • Kamalika Das
    • 2
  • Taneli Mielikäinen
    • 3
  • Donato Malerba
    • 4
  • Jerzy Stefanowski
    • 5
  • Jesse Read
    • 6
  • Marinka Žitnik
    • 7
  • Michelangelo Ceci
    • 8
  • Sašo Džeroski
    • 9
  1. 1.Google ResearchGoogle Inc.ZurichSwitzerland
  2. 2.NASA Ames Research CenterMountain ViewUSA
  3. 3.OathSunnyvaleUSA
  4. 4.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly
  5. 5.Institute of Computing SciencePoznan University of TechnologyPoznanPoland
  6. 6.Laboratoire d’ Informatique (LIX)École PolytechniquePalaiseauFrance
  7. 7.Department of Computer ScienceStanford UniversityStanfordUSA
  8. 8.Università degli Studi di Bari Aldo MoroBariItaly
  9. 9.Jožef Stefan InstituteLjubljanaSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-71273-4
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-71272-7
  • Online ISBN 978-3-319-71273-4
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site