Dr. Thomas Natschläger

NatschlAeger_Thomas.jpg
Key Researcher Data Analysis Systems
Telefon: +43 7236 3343 868
Fax: +43 7236 3343 888
thomas.natschlaeger@scch.at
 

Publikationen

2013
1269H. Kosorus, T. Natschläger, B. Freudenthaler, J. Küng. On the relevance of graphical causa models for failure detection for industrial machinery. In A. Quesada-Arencibia et al. (editor). EUROCAST 2013 Computer Aided Systems Theory Extended Abstracts, pages 85-86, IUCTC Universidad Las Palmas. 2013. 
2012
1049R. Ramler, T. Natschläger. Applying heuristic approaches for predicting defect-prone software components. In R. Moreno-Diaz, F. Pichler, A. Quesada (editors). Computer Aided Systems Theory - Proc. EUROCAST 2011, Part I, Lecture Notes in Computer Science, pages 384-391, Springer. 2012. 
2011
1049aR. Ramler, T. Natschläger. Applying heuristic approaches for predicting defect-prone software components
2010
1048T. Natschläger, T. Steinmaurer, B. Freudenthaler, B. Moser, W. Traunmüller, R. Spolwind, M. Wallnöfer. Energieeffizienz durch Prozess- und Datenanalyse in verteilten mechatronischen Systemen. Tagungsband Industrielles Symposium Mechatronik Automatisierung (ISM 2010), pages on CD, Mechatronik-Cluster. 2010. 
1034R. Ramler, C. Klammer, T. Natschläger. The usual suspects: A case study on delivered defects per developer. Proceedings of the ACM/IEEE 4th Empirical Software Engineering and Measurement Conference (ESEM 2010), ACM. 2010. 
0916R. Ramler, T. Natschläger. Building defect prediction models in practice. Technical Report, SCCH-TR-0916, 2010. 
2009
0909W. Beer, W. Kurschl, F. Matusek, S. Mitsch, B. Moser, T. Natschläger, J. Schönböck, S. Sutor. Application development and management of smart camera networks. In Ahmed Nabil Belbachir (editor). Smart Cameras, pages 259-266, Springer Berlin Heidelberg. 2009. 
0940G. Guta, B. Moser, T. Natschläger. Machine learning extended by semantic reasoning for knowledge discovery of sensory data. Technical Report, SCCH-TR-0940, 2009. 
0914D. Pecevski, T. Natschläger, K. Schuch. PCSIM: A parallel simulation environment for neural. Frontiers in Neuroinformatics, volume 3, number 11, pages doi:10.3389/neuro.11/011.2009, 2009. 
0917R. Ramler, S. Larndorfer, T. Natschläger. What software repositories should be mined for defect predictors?. Proceedings oft the 35th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2009), pages 181-187, IEEE Computer Society. 2009. 
0915R. Ramler, K. Wolfmaier, E. Stauder, F. Kossak, T. Natschläger. Key questions in building defect prediction models in practice. In F. Bomarius, M. Oivo, P. Jaring, P. Abrahamsson (editors). Key Questions in Building Defect Prediction Models in Practice - Proceedings 10th International Confercne PROFES 2009, Lecture Notes in Business Information Processing, pages 14-27, Springer Berlin Heidelberg. 2009. 
2008
0816W. Beer, J. Pichler, T. Natschläger, B. Moser. Integratives wissensbasiertes Modell Lebenszyklus-Management von Kalkulations- und Prozessmodellen: eine Vision. Tagungsband Industrielles Symposium Mechatronik Automatisierung, pages 241-245, Clusterland Oberösterreich GmbH / Mechatronik-Cluster. 2008. 
0819T. Natschläger, J. Himmelbauer, H. Schöner, F. Kokert, S. Mitter, H. Exner. Wissensextraktion mittels maschinellen Lernens zum besseren Prozessverständnis in der Stahlerzeugung. Tagungsband Industrielles Symposium Mechatronik Automatisierung, pages 79-87, Clusterland Oberösterreich GmbH / Mechatronik-Cluster. 2008. 
0818T. Natschläger, W. Traunmüller, K. Reingruber, H. Exner. Lokal optimierte Wetterprognosen zur Regelung stark umweltbeeinflusster Systeme. Tagungsband Industrielles Symposium Mechatronik Automatisierung, pages 281-284, Clusterland Oberösterreich GmbH / Mechatronik-Cluster. 2008. 
2007
0637R. Brette, M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lasner, O. Rochel, T. Vieville, E. Muller, A. Davison, M. Rudolph, S. El Boustani, A. Destexhe, T. Cernevale, M. Hines, D. Beeman, J. Bower, M. Diemann, P. Goodman, F. Harris, Jr.. Simulation ot network of spiking neuros: A review of tools and strategies. Journal fof Cumputational Neuroscience, volume 23, number 3, pages 349-398, 2007. 
0707R. Ramler, K. Wolfmaier, T. Natschläger. Observing distributions in size metrics: Experience from analyzing large software systems. Proc. 31st IEEE Int. Computer Software and Applications Conference, pages 299-304, IEEE Computer Society. 2007. 
2005
0420T. Natschläger, N. Bertschinger, R. Lengstein. At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks. In L.K. Saul, Y. Weiss, L. Bottou (editors). Proc. of NIPS 2004, Advances in Neural Information Processing Systems, pages 145-152, MIT Press. 2005. 
0531T. Natschläger, M. Drobics, F. Kossak. machine learning framework for Mathematica V1.3: Creating interpretable computational models from data. Proc. Wolfram Technology Conf., pages online, 2005. 
0419T. Natschläger, W. Maass. Dynamics of information and emergent computation in generic microcircuit models. Neural Networks, volume 18, number 10, pages 1301-1308, 2005. 
2004
0324N. Bertschinger, T. Natschläger. Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, volume 16, number 7, pages 1413-1436, 2004. 
0425M. Drobics, F. Kossak, T. Natschläger. machine learning framework for Mathematica V1.2 - Creating interpretable computational models from data. Proc. Mathematica Technology Conf., pages online, 2004. 
0430T. Natschläger, N. Bertschinger. Supplementary information to the mean-field theory for randomly connected recurrent network of threshold gates. Technical Report, SCCH-TR-0430, SCCH, 2004. 
0403T. Natschläger, F. Kossak, M. Drobics. Extracting knowledge and computable models from data - Needs, expectations, and experience. Proc. 13th IEEE Int. Conf. on Fuzzy Systems, pages 493 - 498, IEEE Press. 2004. 

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