Machine Learning and Knowledge Discovery in Databases

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

  • Michelangelo Ceci
  • Jaakko Hollmén
  • Ljupčo Todorovski
  • Celine Vens
  • Sašo Džeroski

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10534)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10534)

Table of contents

  1. Front Matter
    Pages I-LXIII
  2. Anomaly Detection

    1. Front Matter
      Pages 1-1
    2. Fabrizio Angiulli
      Pages 3-19
    3. Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton
      Pages 20-35
    4. Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla
      Pages 36-51
    5. Harsh Dani, Jundong Li, Huan Liu
      Pages 52-67
    6. Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, Jürgen Pfeffer
      Pages 68-84
  3. Computer Vision

    1. Front Matter
      Pages 85-85
    2. Wenhe Liu, Xiaojun Chang, Ling Chen, Yi Yang
      Pages 103-118
    3. Adrian Spurr, Emre Aksan, Otmar Hilliges
      Pages 119-134
    4. Mathieu Cliche, David Rosenberg, Dhruv Madeka, Connie Yee
      Pages 135-150
    5. Yun Cao, Zhiming Zhou, Weinan Zhang, Yong Yu
      Pages 151-166
  4. Ensembles and Meta Learning

    1. Front Matter
      Pages 167-167
    2. Anil Narassiguin, Haytham Elghazel, Alex Aussem
      Pages 169-186
    3. Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu
      Pages 187-202
    4. Nicola Di Mauro, Antonio Vergari, Teresa M. A. Basile, Floriana Esposito
      Pages 203-219
  5. Feature Selection and Extraction

    1. Front Matter
      Pages 221-221
    2. Jingkuan Song, Tao He, Hangbo Fan, Lianli Gao
      Pages 223-238
    3. Arvind Kumar Shekar, Tom Bocklisch, Patricia Iglesias Sánchez, Christoph Nikolas Straehle, Emmanuel Müller
      Pages 239-255

Other volumes

  1. Machine Learning and Knowledge Discovery in Databases
    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. 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 algorithm machine learning neural networks recommender systems reinforcement learning signal processing social networking supervised learning Support Vector Machines (SVM) world wide web

Editors and affiliations

  1. 1.Università degli Studi di Bari Aldo MoroBariItaly
  2. 2.Aalto University School of ScienceEspooFinland
  3. 3.University of LjubljanaLjubljanaSlovenia
  4. 4.KU Leuven KulakKortrijkBelgium
  5. 5.Jožef Stefan InstituteLjubljanaSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-71249-9
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-71248-2
  • Online ISBN 978-3-319-71249-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book