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S. Dreiseitl
A comparison of covariate shift detection methods on medical datasets. In Computer Aided Systems Theory—EUROCAST 2022 (LNCS 13789), pages 487–495, Las Palmas, Spain, 2022. |
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S. Dreiseitl
Towards more efficient multiclass AUC computations. In Proceedings of the 31rd European Modeling and Simulation Symposium (EMSS2019), pages 327–331, Lisbon, Portugal, 2019. |
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S. Dreiseitl.
Mathematik für Software Engineering. Springer Vieweg, Berlin, 2018. |
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D. Baumgartner, T. Fischer, R. Riedl, and S. Dreiseitl.
Analysis of heart rate variability (HRV) feature robustness for measuring technostress. In Proceedings of the NeuroIS Retreat 2018, pages 221–228, Vienna, Austria, 2018. |
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T. Schuh and S. Dreiseitl.
Evaluating novel features for aggressive language detection. In Speech and Computer—SPECOM2018 (LNCS 11096), pages 585–595, Leipzig, Germany, 2018. |
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S. Dreiseitl.
Evaluating parallel minibatch training for machine learning applications. In Computer Aided Systems Theory—EUROCAST 2017 (LNCS 10671), pages 400–407, Las Palmas, Spain, 2017. |
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D. Girardi, S. Wartner, G. Halmerbauer, M. Ehrenmüller, H. Kosorus, and S. Dreiseitl.
Using concept hierarchies to improve calculation of patient similarity. Journal of Biomedical Informatics, 63:66–73, 2016. |
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S. Dreiseitl, A. Vieider, and C. Larch.
Using smart grid data to predict next-day energy consumption and photovoltaic production. In Computer Aided Systems Theory—EUROCAST 2015 (LNCS 9520), pages 1–8, Las Palmas, Spain, 2015. |
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N. Niklas, J. Hafenscher, A. Barna, K. Wiesinger, J. Pröll, S. Dreiseitl, S. Preuner-Stix, P. Valent, T. Lion, and C. Gabriel.
cFinder: Definition and quantification of multiple haplotypes in a mixed sample. BMC Research Notes, 8:422, 2015. |
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V. Dorfer, S. Maltsev, S. Dreiseitl, K. Mechtler, and S.M. Winkler.
A symbolic regression based scoring system improving peptide identifications for MS Amanda. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 1335–1341, Madrid, Spain, 2015. |
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S. Dreiseitl.
Feature selection for unsupervised learning via comparison of distance matrices. In Computer Aided Systems Theory—EUROCAST 2013 (LNCS 8111), pages 203–210, Las Palmas, Spain, 2013. |
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J. Weingast, C. Scheibböck, E. Wurm, E. Ranharter, S. Porkert, S. Dreiseitl, C. Posch, and M. Binder.
A prospective study of mobile phones for dermatology in a clinical setting. Journal of Telemedicince and Telecare, 19(4):213–218, 2013. |
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C. Scheibböck, P. Huber, S. Weber, K. Harmankaya,
R. Nemecek, J. Weingast, M. Binder, T. Mehl, C. Schuh, and S. Dreiseitl.
Prediction of metastatic events in patients with cutaneous melanoma. In Proceedings of eTELEMED2013, pages 220–223, Vienna, Austria, 2013. |
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S. Dreiseitl and M. Osl.
Testing the calibration of classification models from first principles. In Proceedings of the AMIA Annual Fall Symposium 2012, pages 164–169, Chicago, USA, 2012. |
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G. Binenbaum, G.S. Ying, G.E. Quinn, J. Huang, S. Dreiseitl, J. Antigua, N. Foroughi, and S. Abbasi.
The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model. Archives of Opthalmology, 130(12):1560–1565, 2012. |
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M. Osl, M. Netzer, S. Dreiseitl and C. Baumgartner.
Applied data mining: From biomarker discovery to decision support systems. In Z. Trajanoski, editor, Computational medicine: tools and challenges, chapter 10, pages 173–184, Springer, 2012. |
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S. Dreiseitl, M. Pivec and M. Binder.
Differences in examination characteristics of pigmented skin lesions: results of an eye tracking study. Artificial Intelligence in Medicine, 54(3):201–205, 2012. |
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S. Dreiseitl and M. Osl.
Effect of reject option on classifier performance. In Proceedings of the 23rd European Modeling and Simulation Symposium (EMSS2011), pages 176–180, Rome, Italy, 2011. |
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S. Dreiseitl and M. Osl.
Effects of data grouping on calibration measures of classifiers. In Computer Aided Systems Theory—EUROCAST 2011 (LNCS 6927), pages 359–366, Las Palmas, Spain, 2011. |
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M. Osl and S. Dreiseitl.
Early diagnosis of acute myocardial infarction using kernel methods. In Proceedings of the 8th IASTED International Conference on Biomedical Engineering, pages 175–180, Innsbruck, Austria, 2011. |
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G. Binenbaum, G. Ying, G.E. Quinn, S. Dreiseitl,
K. Karp, R.S. Roberts, H. Kirpalani, and the PINT study group.
A clinical prediction model to stratify ROP risk using postnatal weight gain. Pediatrics, 127(3):e607–e614, 2011. |
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S. Dreiseitl, M. Osl, C. Baumgartner, and S. Vinterbo.
An evaluation of heuristics for rule ranking. Artificial Intelligence in Medicine, 50(3):175–180, 2010. |
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M. Osl, S. Dreiseitl, J. Kim, K. Patel,
C. Baumgartner, and L. Ohno-Machado.
Effect of data combination on predictive modeling: A study using gene expression data. In Proceedings of the AMIA Annual Fall Symposium 2010, pages 567–571, Washington DC, USA, 2010. |
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S. Dreiseitl, M. Osl, C. Scheibböck, and M. Binder.
Outlier detection with one-class SVMs: An application to melanoma prognosis. In Proceedings of the AMIA Annual Fall Symposium 2010, pages 172–176, Washington DC, USA, 2010. |
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C. Scheibböck, T. Mehl, D. Rafolt, S. Dreiseitl, K. Schlager, J. Weingast, and M. Binder.
Prediction of metastatic disease by computer aided interpretation of tumour markers in patients with malignant melanoma: a feasibility study. In Proceedings of ehealth2010: Health Informatics meets ehealth,pages 161–166, Vienna, Austria, 2010. |
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S. Dreiseitl, K. Auracher, S. Puig, and J. Malvehy.
Modeling of standardized data entry in dermoscopy. In Proceedings of the 21st European Modeling and Simulation Symposium (EMSS2009), pages 184–188, Tenerifa, Spain, 2009. |
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S. Dreiseitl.
Data processing beyond visual interpretation. In Proceedings of the 10th International Congress of Dermatology, pages 81–86, Prague, Czech Republic, 2009. |
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S. Dreiseitl and M. Osl.
Feature selection based on pairwise classification performance. In Computer Aided Systems Theory—EUROCAST 2009 (LNCS 5717), pages 769–776, Las Palmas, Spain, 2009. |
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S. Dreiseitl and L. Ohno-Machado.
Support vector machines. In M.W. Kattan, editor, Encyclopedia of Medical Decision Making, pages 1101–1105. SAGE Publications, 2009. |
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M. Osl, S. Dreiseitl, F. Cerqueira, M. Netzer,
B. Pfeifer, and C. Baumgartner.
Demoting redundant features to improve the discriminatory ability in cancer data. Journal of Biomedical Informatics , 42:721–725, 2009. |
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M. Osl, C. Baumgartner, B. Tilg, and S. Dreiseitl.
On the combination of logistic regression and local probability estimates African Journal of Information and Communication Technology , 5:84–90, 2009. |
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S. Dreiseitl, M. Binder, K. Hable, and H. Kittler.
Computer versus human diagnosis of melanoma: Evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial. Melanoma Research, 19:180–184, 2009. |
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C. Scheibböck, S. Dreiseitl, and M. Binder.
Applicability of mobile phones for teledermatology: a pilot study. In Proceedings of the International Conference on Health Informatics—HEALTHINF 2009, pages 474–477, Porto, Portugal, 2009. |
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M. Osl, C. Baumgartner, B. Tilg, and S. Dreiseitl.
On the combination of logistic regression and local probability estimates. In Proceedings of the 3rd International Conference on Broadband Communications, Information Technology and Biomedical Applications , pages 124–128, Pretoria, South Africa, 2008. |
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M. Osl, L. Ohno-Machado, and S. Dreiseitl.
Improving calibration of logistic regression models by local estimates. In Proceedings of the AMIA Annual Fall Syposium 2008, pages 535–539, Washington DC, USA, 2008. |
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M. Osl, S. Dreiseitl, B. Pfeifer, K. Weinberger,
H. Klocker, G. Bartsch, G. Schaefer, B. Tilg,
A. Graber, and C. Baumgartner.
A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectronomy. Bioinformatics , 24:2908–2914, 2008. |
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S. Dreiseitl, M. Binder, S. Vinterbo, and H. Kittler.
Applying a decision support system in clinical practice: Results from melanoma diagnosis. In Proceedings of the AMIA Annual Fall Symposium 2007, pages 191–195, Chicago, USA, 2007. |
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S. Dreiseitl, K. Auracher, S. Puig, and J. Malvehy.
Computer support for standardized dermoscopy report (poster). First World Meeting of Interdisciplinary Melanoma/Skin Cancer Centers, Barcelona, Spain, 2007. |
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S. Dreiseitl.
Training multiclass classifiers by maximizing the area under the ROC surface. In Computer Aided Systems Theory—EUROCAST2007 (LNCS 4739), pages 878–885, Las Palmas, Spain, 2007. |
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S. Dreiseitl.
Advances in computer-supported melanoma diagnosis: From algorithms to applications. Habilitation, Johannes Kepler Universität Linz, Austria, 2006. |
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M. Binder, H. Kittler, H. Pehamberger, and S. Dreiseitl.
Differentiation between benign and malignant skin tumors by image analysis, neural networks, and other methods of machine learning. In K.-P. Wilhelm et al., editors, Bioengineering of the Skin: Skin Imaging and Analysis, pages 297–304, Informa Healthcare, 2nd edition, 2006. |
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S. Dreiseitl, M. Binder, and H. Kittler.
Investigating the benefits of decision support systems: Lessons from melanoma diagnosis (poster). 28th Annual Meeting of the Society for Medical Decision Making, Boston, USA, 2006. |
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S. Vinterbo and S. Dreiseitl.
A note on solution sizes in the haplotype tagging SNPs problem. In Proceedings of the 2nd European Modeling and Simulation Symposium (EMSS2006), pages 659–663, 2006. |
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S. Vinterbo, S. Dreiseitl and L. Ohno-Machado.
Approximation properties of haplotype tagging. BMC Bioinformatics,7(8), 2006. |
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S. Dreiseitl, A. Harbauer, M. Binder, and H. Kittler.
Nomographic representation of logistic regression models: A case study using patient self-assessment data. Journal of Biomedical Informatics,38:389–394, 2005. |
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S. Dreiseitl and M. Binder.
Do physicians value decision support? A look at the effect of decision support systems on physician opinion. Artificial Intelligence in Medicine,33:25–30, 2005. |
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S.A. Vinterbo, S. Dreiseitl, and L. Ohno-Machado.
A testing procedure for htSNP approximation algorithms. In Proceedings of IDAMAP 2004, pages 101–105, Stanford, USA, 2004. |
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S. Dreiseitl, S. Vinterbo, and L. Ohno-Machado.
Disambiguation data: Extracting information from anonymized sources. Journal of the American Medical Informatics Association, 9:S110–S114, 2002. |
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H. Kittler, S. Dreiseitl, and M. Binder.
How easily can dermatologists be influenced by a decision-support system? (poster). 24th Annual Meeting of the Society for Medical Decision Making, Baltimore, USA, 2002. |
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A. Harbauer, H. Kittler, S. Dreiseitl, and M. Binder.
Evaluating a patient's ability to self-assess melanoma risk (poster). 24th Annual Meeting of the Society for Medical Decision Making, Baltimore, USA, 2002. |
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S. Dreiseitl and L. Ohno-Machado.
Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 35:352–359, 2002. |
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L. Ohno-Machado, S. Vinterbo, S. Dreiseitl, T.K. Jenssen, and W. Kuo.
Comparing imperfect measurements with the Bland-Altman technique: application in gene expression analysis. In Proceedings of the AMIA Annual Fall Syposium 2002, pages 572–576, San Antonio, USA, 2002. |
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L. Ohno-Machado, S. Vinterbo, and S. Dreiseitl.
Effects of data anonymization by cell suppression on descriptive statistics and predictive modeling performance. Journal of the American Medical Informatics Association, 9:S115–S119, 2002. |
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S. Dreiseitl, L. Ohno-Machado, S. Vinterbo, H. Billhardt, and M. Binder.
A comparison of machine learning methods for the diagnosis of pigmented skin lesions. Journal of Biomedical Informatics, 34:28–36, 2001. |
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W. Jacak, K. Pröll, and S. Dreiseitl.
Conflict management in an intelligent multiagent robotics system: Finite state machine approach. In Computer Aided Systems Theory—EUROCAST'01 (LNCS 2178), pages 52–66, 2001. |
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W. Jacak, S. Dreiseitl, K. Pröll, and J. Rozenblit.
Conflict management in multiagent robotic system: FSM and fuzzy logic approach. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'2001), pages 1593–1598, 2001. |
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S. Vinterbo, L. Ohno-Machado, and S. Dreiseitl.
Hiding information by cell suppression. In Proceedings of the AMIA Annual Fall Syposium 2001, pages 726–730, Washington, USA, 2001. |
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S. Dreiseitl, L. Ohno-Machado, and M. Binder.
Comparing three-class diagnostic tests by three-way ROC analysis. Medical Decision Making, 20:323–331, 2000. |
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S. Dreiseitl, H. Kittler, and M. Binder.
Classifying pigmented skin lesions with machine learning methods. In Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, pages 174–179. Springer London, 2000. |
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F. Atienza, N. Martinez-Alzamora, J.A. De Velasco, S. Dreiseitl, and
L. Ohno-Machado.
Risk stratification in heart failure using artificial neural networks. In Proceedings of the AMIA Annual Fall Symposium 2000, pages 32–36, 2000. |
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M. Binder, H. Kittler, S. Dreiseitl, H. Ganster, K. Wolff, and H. Pehamberger.
Computer-aided epiluminescence microscopy of pigmented skin lesions: The value of clincal data for the classification process. Melanoma Research, 10:556–561, 2000. |
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M. Binder and S. Dreiseitl.
Critical appraisal: The interpretation of test results. Journal of Cutaneous Medicine and Surgery, 4:19–25, 2000. |
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L.K. Goodwin, S.G. Maher, L. Ohno-Machado, S. Dreiseitl, S. Vinterbo, M.A.
Iannacchione, W.E. Hammond, and P. Crockett.
Building knowledge in a complex preterm birth problem domain. In Proceedings of the AMIA Annual Fall Symposium 2000, pages 305–309, 2000. |
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K. Pröll, W. Jacak, and S. Dreiseitl.
Negotiation strategies in an intelligent multiagent robotic system. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC'2000), Nashville, TN, USA, 2000. |
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K. Pröll, W. Jacak, and S. Dreiseitl.
Software agent-based intelligent control of robot manipulator. In Proceedings of the International Conference on Intelligent Autonomous Systems (IAS'2000), Venice, Italy, 2000. |
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S. Dreiseitl, L. Ohno-Machado, and S. Vinterbo.
Evaluating variable selection methods for diagnosis of myocardial infarction. In Proceedings of the AMIA Annual Fall Symposium 1999, pages 246–250, 1999. |
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L. Ohno-Machado, S. Vinterbo, A. Ohrn, and S. Dreiseitl.
Clinical data processing tools: A machine learning resource (poster). AMIA Annual Fall Symposium, Washington, USA, 1999. |
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W. Jacak and S. Dreiseitl.
Intelligent robotic agent combining reactive and cognitive capabilities. In S. Tzafestas, editor, Advances in Intelligent Autonomous Systems, pages 93–113. Kluwer Acad. Pub., 1999. |
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W. Jacak, B. Buchberger, and S. Dreiseitl.
Lifelong learning based intelligent robotic agent: Novel neural network approach. In Proceedings of the 1997 Real World Computing Symposium (RWC'97), Tokyo, Japan, 1997. |
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W. Jacak and S. Dreiseitl.
Lifelong learning approach to intelligent agents modeling. In Computer Aided Systems Theory—EUROCAST'97 (LNCS 1333), pages 367–379, Gran Canaria, Spain, 1997. |
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W. Jacak and S. Dreiseitl.
Multisensor reactive robot arm. In Proceedings of the 1st Workshop on Teleoperations and Robotics Applications in Science and Arts, pages 41–54, Linz, Austria, 1997. |
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W. Jacak and S. Dreiseitl.
Intelligent robotic agent combining reactive and cognitive capabilities. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC'97), Orlando, USA, 1997. |
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S. Dreiseitl.
Nonlinear system and structure identification by neural networks and genetic algorithms. PhD thesis, RISC-Linz, Johannes Kepler Universität Linz, Austria, 1997. |
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S. Dreiseitl.
Modeling of discrete dynamical systems by neural networks and genetic algorithms. In Proceedings of the 13th European Meeting on Cybernetics and Systems Research (EMCSR'96), pages 89–94, Vienna, Austria, 1996. |
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S. Dreiseitl.
Discrete dynamical system modeling by evolved neural networks. In Proceedings of the 11th International Conference on Systems Engineering (ICSE'96), pages 19–24, Las Vegas, USA, 1996. |
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W. Jacak, B. Buchberger, S. Dreiseitl, and T. Kubik.
Intelligent robotic arm based on reactive control. In Proceedings of the 5th International Workshop on Robotics in the Alpe-Adria-Danube Region (RAAD'96), pages 297–302, Budapest, Hungary, 1996. |
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W. Jacak and S. Dreiseitl.
Robotic agent control combining reactive and learning capabilities. In Proceedings of the IEEE International Conference on Neural Networks (ICNN'96), Washington, USA, 1996. |
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W. Jacak, S. Dreiseitl, and R. Muszy'nski.
Neural network-based modeling of robot action effects in conceptual state space of real world. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems—IROS'96, pages 1149–1156, Osaka, Japan, 1996. |
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W. Jacak and S. Dreiseitl.
Neural network-based modeling of intelligent robotic agent behavior. In Proceedings of the International Conference on Information Systems Analysis and Synthesis—ISAS'96, pages 82–89, Orlando, USA, 1996. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
and D. Schlosser.
Hybrid evolutionary programming techniques: Application to the design of intelligent robotic agents acting in a real world environment. Technical report, RWCP RISC-Linz, 1996. |
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S. Dreiseitl, W. Jacak, T. Kubik, and R. Muszy'nski.
Neural processing-based robot kinematics modeling and calibration for pose control. In Proceedings of the 12th International Conference on Systems Science, pages 288–295, Wroclaw, Poland, 1995. |
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S. Dreiseitl and W. Jacak.
Genetic algorithm-based neural networks for dynamical system modeling. In Proceedings of the IEEE International Conference on Evolutionary Computing (ICEC'95), pages 602–607, Perth, Australia, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszynski, and
D. Schlosser.
Hybrid programming approach to the design of an intelligent robotic agent acting in the real world. In Proceedings of the 1995 Real World Computing Symposium (RWC'95), pages 15–16, Tokyo, Japan, 1995. (Extended Abstract). |
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W. Jacak and S. Dreiseitl.
Hybrid evolutionary programming—the tools for CAST. In Computer Aided Systems Theory—EUROCAST'95 (LNCS 1030), pages 289–304, Innsbruck, Austria, 1995. |
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W. Jacak, S. Dreiseitl, T. Kubik, and D. Schlosser.
Distributed planning and control of intelligent robot's arm motion based on symbolic and neural processing. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC'95), volume 3, pages 2898–2903, Vancouver, Canada, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
and D. Schlosser.
HYROB System: The module for automatic synthesis of the neural representation of geometric knowledge and fast distance calculation. Technical Report 95-20, RWCP RISC-Linz, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
and D. Schlosser.
HYROB System: The module for automatic synthesis of neural models for robot direct and inverse kinematics. Technical Report 95-21, RWCP RISC-Linz, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
and D. Schlosser.
Hybrid programming approach to robot dynamics. Technical Report 95-22, RWCP RISC-Linz, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
and D. Schlosser.
Neural network-based gain scheduling control system for robots in the presence of uncertainties with one-step trajectory planning. Technical Report 95-23, RWCP RISC-Linz, 1995. |
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B. Buchberger, S. Dreiseitl, I. Duleba, W. Jacak, T. Kubik, R. Muszy'nski,
D. Schlosser, and A. Senanayake.
Neural and symbolic computation-based grasp planning. Technical Report 95-24, RWCP RISC-Linz, 1995. |
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S. Dreiseitl and T. Kubik.
Neural-processed inverse kinematics of robot manipulators. In Proceedings of the Third International Conference on Automation, Robotics, and Computer Vision, volume 3, pages 1748–1751, Singapore, 1994. |
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B. Buchberger, S. Dreiseitl, W. Jacak, T. Kubik, R. Muszy'nski, and
D. Schlosser.
RISC HYROB: The hybrid evolutionary method and system for itelligent robot control. Technical report, RWCP RISC-Linz, 1994. |
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S. Dreiseitl and D. Wang.
Automatic generation of C++ code for neural network simulation. In New trends in neural computing (LNCS 686), pages 358–363, Sitges, Spain, 1993. |
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S. Dreiseitl.
Accelerating the backpropagation algorithm by local methods. Master's thesis, RISC-Linz, Johannes Kepler University Linz, Austria, 1993. |
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S. Dreiseitl.
The backpropagation algorithm: An annotated bibliography. Technical Report 92-39, RISC-Linz, 1992. |