Machine Learning and Knowledge Discovery in Databases

1. Front Matter

   Pages I-XLII

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2. Matrix and Tensor Analysis

   1. Front Matter

      Pages 1-1

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   2. BoostMF: Boosted Matrix Factorisation for Collaborative Ranking

      • Nipa Chowdhury, Xiongcai Cai, Cheng Luo

      Pages 3-18

   3. Convex Factorization Machines

      • Mathieu Blondel, Akinori Fujino, Naonori Ueda

      Pages 19-35

   4. Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond Blocks

      • Pauli Miettinen

      Pages 36-52

   5. Scalable Bayesian Non-negative Tensor Factorization for Massive Count Data

      • Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin

      Pages 53-70

   6. A Practical Approach to Reduce the Learning Bias Under Covariate Shift

      • Van-Tinh Tran, Alex Aussem

      Pages 71-86

   7. Hyperparameter Optimization with Factorized Multilayer Perceptrons

      • Nicolas Schilling, Martin Wistuba, Lucas Drumond, Lars Schmidt-Thieme

      Pages 87-103

   8. Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optimization

      • Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme

      Pages 104-119

   9. Multi-Task Learning with Group-Specific Feature Space Sharing

      • Niloofar Yousefi, Michael Georgiopoulos, Georgios C. Anagnostopoulos

      Pages 120-136

   10. Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

       • Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft

       Pages 137-153

3. Pattern and Sequence Mining

   1. Front Matter

      Pages 155-155

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   2. Fast Generation of Best Interval Patterns for Nonmonotonic Constraints

      • Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli

      Pages 157-172

   3. Non-parametric Jensen-Shannon Divergence

      • Hoang-Vu Nguyen, Jilles Vreeken

      Pages 173-189

   4. Swap Randomization of Bases of Sequences for Mining Satellite Image Times Series

      • Nicolas Méger, Christophe Rigotti, Catherine Pothier

      Pages 190-205

   5. The Difference and the Norm — Characterising Similarities and Differences Between Databases

      • Kailash Budhathoki, Jilles Vreeken

      Pages 206-223

4. Preference Learning and Label Ranking

   1. Front Matter

      Pages 225-225

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   2. Dyad Ranking Using A Bilinear Plackett-Luce Model

      • Dirk Schäfer, Eyke Hüllermeier

      Pages 227-242

   3. Fast Training of Support Vector Machines for Survival Analysis

      • Sebastian Pölsterl, Nassir Navab, Amin Katouzian

      Pages 243-259

   4. Superset Learning Based on Generalized Loss Minimization

      • Eyke Hüllermeier, Weiwei Cheng

      Pages 260-275

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

• data mining
• foundations of machine learning and data mining
• knowledge discovery in databases
• probabilistic models and statistical methods
• social and graphs mining
• classification, regression and supervised learning
• clustering and unsupervised learning
• domain adaptation
• ensemble learning
• large scale learning and big data
• learning paradigms
• machine learning and data mining applications
• machine learning methodologies
• meta-learning
• nonmonotonic constraints
• pattern and sequence mining
• privacy-preserving data mining
• probabilistic programming
• recommender systems
• rich data mining

• University of Bari Aldo Moro, Bari, Italy

  Annalisa Appice

• University of Porto, Porto, Portugal

  Pedro Pereira Rodrigues

• Universidade do Porto, Porto, Portugal

  Vítor Santos Costa

• University of Porto - INESC TEC, Porto, Portugal

  João Gama, Alípio Jorge, Carlos Soares

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