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Machine Learning and Knowledge Discovery in Databases

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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Ulf Brefeld

    1. Leuphana University, Lüneburg, Germany

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  2. Edward Curry

    1. National University of Ireland, Galway, Ireland

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  3. Elizabeth Daly

    1. IBM Research - Ireland, Dublin, Ireland

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  4. Brian MacNamee

    1. University College Dublin, Dublin, Ireland

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  5. Alice Marascu

    1. Nokia (Ireland), Dublin, Ireland

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  6. Fabio Pinelli

    1. Vodafone, Milan, Italy

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  7. Michele Berlingerio

    1. IBM Research - Ireland, Dublin, Ireland

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  8. Neil Hurley

    1. University College Dublin, Dublin, Ireland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11053)

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

Included in the following conference series:

Conference proceedings info: ECML PKDD 2018.

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Table of contents (54 papers)

  1. Front Matter

    Pages I-XXXI
    Download chapter PDF

  2. ADS Data Science Applications

    1. Front Matter

      Pages 1-1
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    2. Neural Article Pair Modeling for Wikipedia Sub-article Matching

      • Muhao Chen, Changping Meng, Gang Huang, Carlo Zaniolo
      Pages 3-19

    3. LinNet: Probabilistic Lineup Evaluation Through Network Embedding

      • Konstantinos Pelechrinis
      Pages 20-36

    4. Improving Emotion Detection with Sub-clip Boosting

      • Ermal Toto, Brendan J. Foley, Elke A. Rundensteiner
      Pages 37-52

    5. Machine Learning for Targeted Assimilation of Satellite Data

      • Yu-Ju Lee, David Hall, Jebb Stewart, Mark Govett
      Pages 53-68

    6. From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth

      • Hau Chan, Eric Rice, Phebe Vayanos, Milind Tambe, Matthew Morton
      Pages 69-85

    7. Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping

      • Artur Mrowca, Barbara Moser, Stephan Günnemann
      Pages 86-102

  3. ADS E-commerce

    1. Front Matter

      Pages 103-103
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    2. SPEEDING Up the Metabolism in E-commerce by Reinforcement Mechanism DESIGN

      • Hua-Lin He, Chun-Xiang Pan, Qing Da, An-Xiang Zeng
      Pages 105-119

    3. Discovering Bayesian Market Views for Intelligent Asset Allocation

      • Frank Z. Xing, Erik Cambria, Lorenzo Malandri, Carlo Vercellis
      Pages 120-135

    4. Intent-Aware Audience Targeting for Ride-Hailing Service

      • Yuan Xia, Jingbo Zhou, Jingjia Cao, Yanyan Li, Fei Gao, Kun Liu et al.
      Pages 136-151

    5. A Recurrent Neural Network Survival Model: Predicting Web User Return Time

      • Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain
      Pages 152-168

    6. Implicit Linking of Food Entities in Social Media

      • Wen-Haw Chong, Ee-Peng Lim
      Pages 169-185

    7. A Practical Deep Online Ranking System in E-commerce Recommendation

      • Yan Yan, Zitao Liu, Meng Zhao, Wentao Guo, Weipeng P. Yan, Yongjun Bao
      Pages 186-201

  4. ADS Engineering and Design

    1. Front Matter

      Pages 203-203
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    2. Helping Your Docker Images to Spread Based on Explainable Models

      • Riccardo Guidotti, Jacopo Soldani, Davide Neri, Antonio Brogi, Dino Pedreschi
      Pages 205-221

    3. ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction

      • Haytham Assem, Bora Caglayan, Teodora Sandra Buda, Declan O’Sullivan
      Pages 222-237

    4. On Optimizing Operational Efficiency in Storage Systems via Deep Reinforcement Learning

      • Sunil Srinivasa, Girish Kathalagiri, Julu Subramanyam Varanasi, Luis Carlos Quintela, Mohamad Charafeddine, Chi-Hoon Lee
      Pages 238-253

    5. Automating Layout Synthesis with Constructive Preference Elicitation

      • Luca Erculiani, Paolo Dragone, Stefano Teso, Andrea Passerini
      Pages 254-270
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Other volumes

  1. Machine Learning and Knowledge Discovery in Databases

  2. Machine Learning and Knowledge Discovery in Databases

  3. Machine Learning and Knowledge Discovery in Databases

  4. ECML PKDD 2018 Workshops

  5. ECML PKDD 2018 Workshops

Keywords

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 learning; ensemble 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.

Editors and Affiliations

  • Leuphana University, Lüneburg, Germany

    Ulf Brefeld

  • National University of Ireland, Galway, Ireland

    Edward Curry

  • IBM Research - Ireland, Dublin, Ireland

    Elizabeth Daly, Michele Berlingerio

  • University College Dublin, Dublin, Ireland

    Brian MacNamee, Neil Hurley

  • Nokia (Ireland), Dublin, Ireland

    Alice Marascu

  • Vodafone, Milan, Italy

    Fabio Pinelli

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