2018

L. Adilova, S. Giesselbach, and S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. arXiv:1807.04687 [cs.LG], 2018.

C. Bauckhage, C. Ojeda, R. Sifa, and S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. Proc. LWDA, 2018.

C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, and S. Wrobel: Informed Machine Learning Through Functional Composition. Proc. LWDA, 2018.

M. Bunse, N. Piatkowski, and K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. Proc. LWDA, 2018.

S. Buschjäger and K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. IEEE Trans. on Circuits and Systems 65-I(1), 2018.

M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, and S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, and S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: G. Leventakis and M. Haberfeld (eds) Community-Oriented Policing and Technological Innovations, Springer, 2018.

K. Morik, C. Bockermann, and S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, and K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. Proc. SIAM Int. Conf. on Data Mining (SDM), 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, and S. Wrobel: Policy Learning Using SPSA. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

E. Schubert, S. Hess, and K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. Proc. LWDA, 2018.

R. Sifa, D. Paurat, D. Trabold, and C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

P. Welke, T. Horvath, and S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 2018.

B. Wulff, J. Schücker, and C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

2017

C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf. on Artificial Neural Networks (ICANN),

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. Proc. Int. Conf. on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017

S. Hess and K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),

S. Hess, K. Morik, and N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. Data Mining and Knowledge Discovery 31(4), 2017.

T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. Information Systems 64, 2017.

L. Pfahler, K. Morik, F. Elwert, S. Tabti, and V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

R. Sifa and C. Bauckhage: Online k-Maxoids Clustering. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, and S. Wrobel: Co-Regularised Support Vector Regression. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2017.

2016

M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting: Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 2016.

N. Piatkowski and K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. Proc. Int. Conf on Machine Learning (ICML), 2016.

N. Piatkowski, S. Lee, and K. Morik: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, and K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. Machine Learning 104(2-3), 2016.

R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, and C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. Proc. IEEE Int Conf. on Computational Intelligence and Games (CIG), 2016.

 

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