Machine Learning and Knowledge Discovery in Databases

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 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.

• 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)

• Google Research, Google Inc., Zurich, Switzerland

  Yasemin Altun

• NASA Ames Research Center, Mountain View, USA

  Kamalika Das

• Oath, Sunnyvale, USA

  Taneli Mielikäinen

• Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

  Donato Malerba

• Institute of Computing Science, Poznan University of Technology, Poznan, Poland

  Jerzy Stefanowski

• Laboratoire d’ Informatique (LIX), École Polytechnique, Palaiseau, France

  Jesse Read

• Department of Computer Science, Stanford University, Stanford, USA

  Marinka Žitnik

• Università degli Studi di Bari Aldo Moro, Bari, Italy

  Michelangelo Ceci

• Jožef Stefan Institute, Ljubljana, Slovenia

  Sašo Džeroski

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