Machine Learning and Knowledge Discovery in Databases

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.

• 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)

• IBM Research - Ireland, Dublin, Ireland

  Michele Berlingerio

• Institute for Scientific Interchange, Turin, Italy

  Francesco Bonchi

• University of Nottingham, Nottingham, UK

  Thomas Gärtner

• University College Dublin, Dublin, Ireland

  Neil Hurley, Georgiana Ifrim

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