Publikationen im Bereich Maschinelles Lernen, Data Mining und Data Analytics

Publikationen im Bereich Maschinelles Lernen, Data Mining und Data Analytics

  • M. Rottmann, K. Kahl, H. Gottschalk. Deep Bayesian Active Semi-Supervised Learning, arXiv:1803.01216
  • K. Kahl, M. Rottmann. Least Angle Regression Coarsening in Bootstrap Algebraic Multigrid, arXiv:1802.00595, submitted
  • J. Cao1, A. Kummert, Z. Lin, J. Velten. Journal of the Franklin Institute: Special Issue on Recent Advances in Machine Learning for Signal Analysis and Processing
  • K. Klamroth, E. Köbis, A. Schöbel, C. Tammer. A unified approach to uncertain optimization. European Journal of Operational Research 260:403-420, 2017
  • S. Bracke, B. Backes. Multivariate process capability, process validation and risk analytics based on product characteristic sets: case study piston rod. In: A. Burduk and D. Mazurkiewicz (Eds.): Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017, Wrocław, Poland; 28th – 29th September 2017, Springer.
  • A. Gavriilidis, J. Velten, S. Tilgner, A. Kummert. Machine learning for people detection in guidance functionality of enabling health applications by means of cascaded SVM classifiers in: Journal of the Franklin Institute, 2017
  • J. Heinrich, F. Plinke, J. Hauschild. State-based safety and availability analysis of automated driving functions using Monte Carlo Simulation and Semi-Markov-Processes. Beitrag European Safety and Reliability Conference, Portoroz, Slowenien, 2017.
  • D. Wagner, K. Kalischewski, J. Velten, A. Kummert. Activity recognition using inertial sensors and a 2-D convolutional neural network, in: Multidimensional (nD) Systems (nDS), 2017 10th International Workshop on, pages 1-6, 2017
  • F. Ghorban, S. Farzin, Y. Su, M. Meuter, A. Kummert. Insatiate boosted forest: Towards data exploitation in object detection, in: 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pages 331 – 338, 2017
  • A. Braasch, D. Althaus. Automatisiertes und Vernetztes Fahren – Neue Herausforderungen an Sicherheit und Zuverlässigkeit. Beitrag VDI Mechatronik Tagung, Dresden, 2017.
  • J. Ringel, A. Witte. Mitgefühl im Schadenfall. Versicherungswirtschaft, 2017.
  • J. Frochte, I. Bernst. Success Prediction System for Student Counseling Using Data Mining in the Proceedings of the 8th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2016), November 2016 in Porto Portugal; pages 181-189, INSTICC, ISBN 978-989-758-203-5
  • M. Hinz, F. Hienzsch, S. Bracke. Analysis of simulated and recorded data of car fleets based on machine learning algorithms. Proceedings: The 13th Probabilistic Safety Assessment and Management, PSAM 13, Seoul, Korea, October 2nd -7th, 2016.
  • I. Bernst, C. Kaufmann, J. Frochte. On Learning Assistance Systems for Numerical Simulation published in International Journal on Computer Science and Information Systems, 11.1 (2016), S. 115–133. ISSN: 1646-3692
  • F. Plinke. Beitrag zur Weiterentwicklung der zuverlässigkeitstechnischen Sensitivitäts- und Ausfallanalyse mittels Monte-Carlo-Simulation. Dissertation, Bergische Universität Wuppertal, 2016
  • A. Braasch. Autonomes Fahren – Rechtliche und Gesellschaftliche Herausforderung, Beitrag VDI Arbeitskreis „Risikomanagement und Zuverlässigkeit“. Wuppertal, 2016.
  • K. Dächert, K. Klamroth. A linear bound on the number of scalarizations needed to solve discrete tricriteria optimization problems. Journal of Global Optimization 61:643–676, 2015
  • S. Burrows, J. Frochte, M. Völske, A. B. Martínez Torres, B. Stein. Learning Overlap Optimization for Domain Decomposition Methods in Proc. of the Seventeenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), volume 7818 of LNAI, pages 438-449, Gold Coast, Australia, April 2013. Springer
  • F. Pfeuffer, M. Stiglmayr, K. Klamroth. Discrete and geometric Branch and Bound algorithms for medical image registration. Annals of Operations Research, 196(1):737-765, 2012.
  • S. Burrows, B. Stein, J. Frochte, D. Wiesner, K. Müller. Simulation Data Mining for Supporting Bridge Design in Proc. Australasian Data Mining Conference (AusDM 11), Ballarat, Australia pages 71-79, December 2011. ACM. ISBN 978-1-921770-02-9
  • O. Museyko, M. Stiglmayr, K. Klamroth, G. Leugering. On the Application of the Monge-Kantorovich Problem to Image Registration. SIAM Journal on Imaging Sciences 2:1068-1097, 2009.

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