@inproceedings{13153,
  author       = {{Graf, Tobias and Platzner, Marco}},
  booktitle    = {{Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015, Revised Selected Papers}},
  pages        = {{1--11}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Adaptive Playouts in Monte-Carlo Tree Search with Policy-Gradient Reinforcement Learning}}},
  doi          = {{10.1007/978-3-319-27992-3_1}},
  year         = {{2015}},
}

@misc{13220,
  author       = {{Webersen, Manuel and Bause, Fabian and Henning, Bernd}},
  title        = {{{Application of Automatic Differentiation for the inverse parameter identification of complex-valued forward models}}},
  year         = {{2015}},
}

@inproceedings{13222,
  abstract     = {{When performing measurements, the effects of the measurement system itself on the measured data generally must be eliminated. Consequently, those effects, i.e. the system’s dynamic behavior, need to be known. For the piezo-composite transducers in an ultrasonic transmission line, a model based approach is used to describe their dynamic behavior and take into account its dependence on the environment temperature and the acoustic impedance of the target medium. Temperature-dependent model parameters are presented, which are obtained by performing a multiplepart identification process on the transducer model, based on electrical impedance measurements [1]. The identification process uses an inverse approach for optimizing a subset of the model parameters. Additionally, algorithmic differentiation methods are used to determine accurate derivatives. In a final optimization step, impedance measurements taken at different temperatures are used to determine the temperature dependencies of the model parameters. These can then be used to assess the plausibility of the identification results. Additionally, the parameters can be expressed as polynomials in the temperature to take different operating conditions into account.}},
  author       = {{Webersen, Manuel and Bause, Fabian and Rautenberg, Jens and Henning, Bernd}},
  booktitle    = {{AMA Conferences 2015}},
  keywords     = {{piezo-composite, transducer, temperature dependency, identification, plausibility}},
  location     = {{Nürnberg}},
  pages        = {{195--200}},
  title        = {{{Identification of temperature-dependent model parameters of ultrasonic piezo-composite transducers}}},
  year         = {{2015}},
}

@misc{13224,
  author       = {{Webersen, Manuel and Karzellek, Michael and Bause, Fabian and Henning, Bernd}},
  title        = {{{Implementation and uncertainty analysis of a test device for electrical impedance measurements using vector network analyzers}}},
  year         = {{2015}},
}

@inproceedings{10234,
  author       = {{Hüllermeier, Eyke and Minor, M.}},
  booktitle    = {{in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343}},
  publisher    = {{Springer}},
  title        = {{{Case-Based Reasoning Research and Development }}},
  year         = {{2015}},
}

@inproceedings{10235,
  author       = {{Hoffmann, F. and Hüllermeier, Eyke}},
  title        = {{{Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing}}},
  year         = {{2015}},
}

@inproceedings{10236,
  author       = {{Abdel-Aziz, A. and Hüllermeier, Eyke}},
  booktitle    = {{In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015)}},
  pages        = {{1--14}},
  title        = {{{Case Base Maintenance in Preference-Based CBR}}},
  year         = {{2015}},
}

@inproceedings{10237,
  author       = {{Szörényi, B. and Busa-Fekete, Robert and Weng, P. and Hüllermeier, Eyke}},
  booktitle    = {{In Proceedings International Conference on Machine Learning (ICML 2015)}},
  pages        = {{1660--1668}},
  title        = {{{Qualitative Multi-Armed Bandits: A Quantile-Based Approach}}},
  year         = {{2015}},
}

@inproceedings{10238,
  author       = {{Schäfer, Dirk and Hüllermeier, Eyke}},
  booktitle    = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}},
  pages        = {{227--242}},
  title        = {{{Dyad Ranking Using A Bilinear Plackett-Luce Model}}},
  year         = {{2015}},
}

@inproceedings{10239,
  author       = {{Hüllermeier, Eyke and Cheng, W.}},
  booktitle    = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}},
  pages        = {{260--275}},
  title        = {{{Superset Learning Based on Generalized Loss Minimization }}},
  year         = {{2015}},
}

@inproceedings{10240,
  author       = {{Henzgen, Sascha and Hüllermeier, Eyke}},
  booktitle    = {{in Proceedings European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD)}},
  pages        = {{422--437}},
  title        = {{{Weighted Rank Correlation : A Flexible Approach Based on Fuzzy Order Relations}}},
  year         = {{2015}},
}

@inproceedings{10241,
  author       = {{Szörényi, B. and Busa-Fekete, Robert and Paul, Adil and Hüllermeier, Eyke}},
  booktitle    = {{in Advances in Neural Information Processing Systems 28 (NIPS 2015)}},
  pages        = {{604--612}},
  title        = {{{Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach}}},
  year         = {{2015}},
}

@inproceedings{10242,
  author       = {{Szörényi, B. and Busa-Fekete, Robert and Dembczynski, K. and Hüllermeier, Eyke}},
  booktitle    = {{in Advances in Neural Information Processing Systems 28 (NIPS 2015)}},
  pages        = {{595--603}},
  title        = {{{Online F-Measure Optimization}}},
  year         = {{2015}},
}

@inproceedings{10243,
  author       = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}},
  booktitle    = {{in Workshop Proc. 23rd International Conference on Case-Based Reasoning (ICCBR 2015)}},
  pages        = {{68--77}},
  title        = {{{A CBR Approach to the Angry Birds Game}}},
  year         = {{2015}},
}

@inproceedings{10244,
  author       = {{Schäfer, Dirk and Hüllermeier, Eyke}},
  booktitle    = {{in Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel@PKDD/ECML)}},
  pages        = {{110--111}},
  title        = {{{Preference-Based Meta- Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations}}},
  year         = {{2015}},
}

@inproceedings{10245,
  author       = {{Lu, S. and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings 25. Workshop Computational Intelligence}},
  pages        = {{97--104}},
  title        = {{{Locally weighted regression through data imprecisiation}}},
  year         = {{2015}},
}

@inproceedings{10246,
  author       = {{Ewerth, Ralph and Balz, A. and Gehlhaar, J. and Dembczynski, K. and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings 25. Workshop Computational Intelligence}},
  pages        = {{235--240}},
  title        = {{{Depth estimation in monocular images: Quantitative versus qualitative approaches}}},
  year         = {{2015}},
}

@article{10319,
  author       = {{Waegeman, W. and Dembczynski, K. and Jachnik, A. and Cheng, W. and Hüllermeier, Eyke}},
  journal      = {{in Journal of Machine Learning Research}},
  pages        = {{3333--3388}},
  title        = {{{On the Bayes-Optimality of F-Measure Maximizers}}},
  volume       = {{15}},
  year         = {{2015}},
}

@article{10320,
  author       = {{Hüllermeier, Eyke}},
  journal      = {{Fuzzy Sets and Systems}},
  pages        = {{292--299}},
  title        = {{{Does machine learning need fuzzy logic?}}},
  volume       = {{281}},
  year         = {{2015}},
}

@article{10321,
  author       = {{Shaker, Ammar and Hüllermeier, Eyke}},
  journal      = {{Neurocomputing}},
  pages        = {{250--264}},
  title        = {{{Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study}}},
  volume       = {{150}},
  year         = {{2015}},
}