OFAI

Publications


Anderer P., Gruber G., Parapatics S., Woertz M., Miazhynskaia T., Kloesch G., Saletu B., Zeitlhofer J., Barbanoj M., Danker-Hopfe H., Himanen S., Kemp B., Penzel T., Groezinger M., Kunz D., Rappelsberger P., Schloegl A., Dorffner G.: An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 x 7 Utilizing the Siesta Database., Neuropsychobiology, 51(3)115-133, 2005.

Anderer P., Roberts S., Schloegl A., Gruber G., Kloesch G., Herrmann W., Rappelsberger P., Filz O., Barbanoj M.J., Dorffner G., Saletu B.: Artifact processing in computerized analysis of sleep EEG - a review, Neuropsychobiology, 40(3)150-157, 1999.

Bauer H., Flexer A., Guttmann G., Leodolter M., Leodolter U., Vitouch O.: Categorization of Topographical Patterns of Spontaneous Cortical Slow Potential Shifts (sSPSs) (abstract), Brain Topography, 1994.

Cristianini N., Shawe-Taylor J., Sykacek P.: Bayesian Classifiers are Large Margin Hyperplanes in a Hilbert Space, in Shavlik J.(ed.), Proceedings of the 15th International Conference on Machine Learning (ICML '98), Morgan Kaufmann, Los Altos/Palo Alto/San Francisco, pp.109-117, 1998.

Danker-Hopfe H., Kunz D., Gruber G., Kloesch G., Lorenzo J.L., Himanen S.L., Kemp B., Penzel T., Roeschke J., Dorn H., Schloegl A., Trenker E., Dorffner G.: Interrater reliability between scorers from eight European sleep laboratories in subjects with different sleep disorders, Journal of Sleep Research, 13(1)63-69, 2004.

Danker-Hopfe H., Schaefer M., Dorn H., Anderer P., Saletu B., Gruber G., Zeitlhofer J., Kunz D., Barbanoj M., Himanen S.L., Kemp B., Penzel T., Roeschke J., Dorffner G.: Percentile reference charts for selected sleep parameters for 20- to 80-year-old healthy subjects from the SIESTA database, Somnologie, 9(1)3-14, 2005.

Dawid H., Doerner K., Dorffner G., Fent T., Feurstein M., Hartl R., Mild A., Natter M., Reimann M., Taudes A.: Quantitative Models of Learning Organizations, Springer Wien/New York, 2002.

Dorffner G. (ed.): Konnektionismus in Artificial Intelligence und Kognitionsforschung, Springer, Berlin/Heidelberg/New York/Tokyo, 1990.

Dorffner G.: A Radical View on Connectionist Language Modeling, in Dorffner G.(ed.), Konnektionismus in Artificial Intelligence und Kognitionsforschung, Springer, Berlin/Heidelberg/New York/Tokyo, pp.217-220, 1990.

Dorffner G.: Konnektionismus, Teubner, Stuttgart, 1991.

Dorffner G.: "Radical" Connectionism for Natural Language Processing, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1991.
http://www.ofai.at/cgi-bin/tr-online?number+91-07

Dorffner G.: Taxonomies and Part-Whole Hierarchies in the Acquisition of Word Meaning - A Connectionist Model, in Proceedings of the 14th Annual Conference of the Cognitive Science Society, Lawrence Erlbaum, New Haven/Hillsdale/Hove, pp.803-808, 1992.
http://www.ofai.at/cgi-bin/tr-online?number+92-38

Dorffner G.: A Step Toward Sub-Symbolic Language Models without Linguistic Representations, in Reilly R. and Sharkey N.(eds.), Connectionist Approaches to Natural Language Processing, Vol.1, Lawrence Erlbaum, New Haven/Hillsdale/Hove, 1992.

Dorffner G.: An Introduction to Neurocomputing and its Possible Role in AI, in Marik V., et al.(eds.), Advanced Topics in Artificial Intelligence, Springer, Berlin/Heidelberg/New York/Tokyo, pp.440-464, 1992.

Dorffner G.: On Redefining Symbols and Reuniting Connectionism with Cognitively Plausible Symbol Manipulations, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1992.

Dorffner G.: "Winner-Take-More" - A Mechanism for Soft Competitive Learning, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1992.
http://www.ofai.at/cgi-bin/tr-online?number+92-12

Dorffner G.: EuclidNet - A Multilayer Neural Network using the Euclidian Distance as Propagation Rule, in Aleksander I. and Taylor J.(eds.), Artificial Neural Networks, 2, North-Holland, Amsterdam/New York, pp.1633-1636, 1992.

Dorffner G.: Review on: Neural Networks for Vision, Speech and Natural Language, Connection Science, 1993.

Dorffner G.: Neuronale Netzwerke fuer Diagnose und Monitoring, OCG-Kommunikativ, 1993.

Dorffner G.: Neural Networks for EEG Classification: An Introduction (abstract), Journal of Clinical Monitoring, 1993.

Dorffner G.: Connectionism and Syntactic Binding of Concepts, Behavioral and Brain Sciences, 16(3)456-457, 1993.

Dorffner G.: How Connectionism Can Change AI and the Way We Think about Ourselves, in Trappl R., Artificial Intelligence: Future, Impacts, Challenges, Applied Artificial Intelligence,, 59-85, 1993.

Dorffner G.: Neural Networks for EEG Classification: An Introduction, in Loeffler W.H., et al.(eds.), ZNS-Monitoring, Verlag Wilhelm Maudrich, Wien-Muenchen-Bern, pp.319-324, 1993.

Dorffner G.: Repraesentation und Selbstorganisation im Konnektionismus, in Duwe I., et al.(eds.), Konnektionismus und Neuronale Netze, GMD, Sankt Augustin, Germany, pp.213-228, 1994.

Dorffner G.: Why Connectionism and Language Modeling Need DETE, Connection Science, 6(1)115-118, 1994.

Dorffner G.: A Unified Framework for of MLPs and RBFNs: Introducing Conic Section Function Networks, Cybernetics and Systems,, 511-554, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+93-25

Dorffner G.: A generalized view on learning in feedforward neural networks, in Cromme L., et al.(eds.), CoWAN'94, Technische Universitaet Cottbus, Reihe Mathematik M-01/1995, pp.34-54, 1995.
http://www.ofai.at/cgi-bin/tr-online?number+95-20

Dorffner G.: Classification through Hyperplane Fitting with Feedforward Neural Networks, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1995.
http://www.ofai.at/cgi-bin/tr-online?number+95-19

Dorffner G.: Categorization in early language acquisition - accounts from a connectionist model, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1996.
http://www.ofai.at/cgi-bin/tr-online?number+96-16

Dorffner G.: Neural networks for time series processing, Neural Network World, 6(4)447-468, 1996.
http://www.ofai.at/cgi-bin/tr-online?number+96-17

Dorffner G.: Radical Connectionism - A Neural Bottom-Up Approach to AI, in Dorffner G.(ed.), Neural Networks and a New AI, International Thomson Computer Press, London, 1997.

Dorffner G.: Can neural networks improve signal processing? A critical assessment from the ANNDEE project., Proc. of Measurement '97, 1997.

Dorffner G.: Artificial neural networks for EEG processing - the ANNDEE report (abstract), Proc. of 4th European Conf. on Engineering and Medicine, 1997.

Dorffner G. (ed.): Neural Networks and a New AI, International Thomson Computer Press, London, 1997.

Dorffner G.: Toward a new standard of modeling sleep based on polysomnograms - the SIESTA project (abstract), Electroencephalography and Clinical Neurophysiology, 106, suppl. 1001, p.28, 1998.

Dorffner G.: Flexible features, connectionism and computational learning theory, Behavioral and Brain Sciences, 21(1)24, 1998.

Dorffner G.: The connectionist route to embodiment and dynamicism, in Riegler A., et al., Understanding Representation in the Cognitive Sciences, Kluwer, Boston/Dordrecht/London, pp.23-32, 1999.

Dorffner G.: The subsymbolic approach to ANN-based natural language processing, in Dale R., et al., Handbook of Natural Language Processing, Marcel Dekker Inc., New York, pp. 785-822, 2000.

Dorffner G., Bischof H., Hornik K. (eds.): Artificial Neural Networks - ICANN 2001, International Conference, Vienna, Austria, Lecture Notes In Computer Science 2130, Springer, 2001.

Dorffner G., Harris C.L.: When pseudowords become words - effects of learning on orthographic similarity priming, in Shafto M.G. and Langley P.(eds.), Proceedings of the 19th Annual Conference of the Cognitive Science Society, Lawrence Erlbaum, New Haven/Hillsdale/Hove, pp.185-190, 1997.

Dorffner G., Hentze M., Thurner G.: A Connectionist Model of Categorization and Grounded Word Learning, in Koster C., Wijnen F. (eds.): Proceedings of the Groningen Assembly on Language Acquisition (GALA '95), 1996.
http://www.ofai.at/cgi-bin/tr-online?number+96-15

Dorffner G., Leitgeb E., Koller H.: Toward Improving Exercise ECG for Detecting Ischemic Heart Disease with Recurrent and FeedForward Neural Nets, in Vlontzos J., et al.(eds.), Neural Networks for Signal Processing IV, Institute of Electrical and Electronics Engineers, Inc., New York, NY, pp.499-508, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+94-23

Dorffner G., Leitgeb E., Koller H.: A comparison of linear and non-linear classifiers for the detection of coronary artery disease in stress-ECG, in Horn W., et al.(eds.), Artificial Intelligence in Medicine, Springer, Berlin, pp.227-231, 1999.

Dorffner G., Moeller K., Paass G., Vogel S. (eds.): Konnektionismus und Neuronale Netze, GMD-Studien, 1995.

Dorffner G., Porenta G.: On Using Feedforward Neural Networks for Clinical Diagnostic Tasks, Artificial Intelligence in Medicine, 6(5), Special Issue on Neurocomputing in Medicine, pp. 417-435, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+93-23

Dorffner G., Prem E.: Connectionism, Symbol Grounding, and Autonomous Agents, Proc. of 15th Annual Conference of the Cognitive Science Society, Boulder, CO, June, Lawrence Erlbaum Associates, pp. 144-148, 1993.

Dorffner G., Prem E., Mackinger M., Kundrat S., Petta P., Porenta G., Sochor H.: Experiences with Neural Networks as a Diagnostic Tool in Medical Image Processing, in Michaelis J., et al.(eds.), Europaeische Perspektiven der Medizinischen Informatik, Biometrie und Epidemologie, MMV, Muenchen, 1993.
http://www.ofai.at/cgi-bin/tr-online?number+93-12

Dorffner G., Prem E., Trost H.: Words, Symbols, and Symbol Grounding, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, TR- 93-30, 1993.
http://www.ofai.at/cgi-bin/tr-online?number+93-30

Dorffner G., Prem E., Ulbricht C., Wiklicky H.: Theory and Practice of Neural Networks, in Brauer W. and Hernandez D.(eds.), Verteilte Kuenstliche Intelligenz und kooperatives Arbeiten, Springer, Berlin/Heidelberg/New York/Tokyo, 1991.

Dorffner G., Rappelsberger P., Flexer A.: Using Selforganizing Feature Maps to Classify EEG Coherence Maps, in Kappen B. and Gielen S.(eds.), ICANN 93, Springer, Berlin/Heidelberg/New York/Tokyo, pp.882-887, 1993.

Dorffner G., Rotter M.: On the Virtues of Functional Connectionist Compositionality, in Neumann B.(ed.), Proc. of 10th European Conference on Artificial Intelligence (ECAI92), Wiley, Chichester/London/New York, pp.203-205, 1992.

Dorffner G., Schellner K., Prem E.: Regularized Gaussian Mixture Models for Effective Short-Term Forecasting of Rainfall Pattern, Elektrotechnik und Informationstechnik, 118(7/8)371-378, 2001.

Dorffner G., Schoenauer T.: Unsupervised Learning of Simple Speech Production Based of Soft Competitive Learning, in Eeckman F.H. and Bower J.M.(eds.), Computation and Neural Systems, Kluwer, Dordrecht/Boston/London, pp.363-368, 1993.
http://www.ofai.at/cgi-bin/tr-online?number+93-11

Dorffner G., Stoecklmayer C., Schmidt C., Schima H.: Synergies between statistical data analysis and neural networks in the control of rotary blood pumps, Cybernetics and Systems, 28(3) 215-224, 1997.
http://www.ofai.at/cgi-bin/tr-online?number+94-22

Dorffner G., Wiklicky H.: Reanalyzing Similarity Measures in Neural Networks and Their Practical Consequences, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1992.
http://www.ofai.at/cgi-bin/tr-online?number+92-15

Dorffner G., Wiklicky H., Prem E.: Formal Neural Network Specification and its Implications on Standardization, Computer Standards and Interfaces, 16, spec. issue on Artificial Neural Network Standards, pp.205-219, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+93-24

Dorffner R., Neumann C., Gergely I., Renner R., Juhasz M., Resinger M., Dorffner G.: Implantation of the Corinthian IQ stent into the femeropoplietal arteries using 6-F introducer sheaths and crossover procedures: midterm results, European Radiology, 13 (11) 2535-2539, 2003.

Fetty G.: Systemintegrierte statistische Modellselektion und Vorverarbeitung fuer neuuronale Netze, Institut fuer Med.Kybernetik u. AI, Universitaet Wien, 1997.

Findl O., Struhal W., Dorffner G., Drexler W.: Analysis of non-linear systems to estimate the intraocular lens position after cataract surgery, Journal of Cataract and Refractive Surgery, 30(4)863-866, 2004.

Flexer A.: Die Kategorisierung von EEG-Kohaerenzmaps mit Kohonennetzwerken, OeGAI Journal, 13(2)9-12, 1994.

Flexer A.: Die Kategorisierung von EEG-Kohaerenzmaps mit Kohonennetzwerken, Universitaet Wien, 1994.

Flexer A.: Non-Representationalism and Novel-Representationalism are basically the same (abstract), Abstracts book of the European Conference on Artificial Life 95, 1995.

Flexer A.: Connectionists and Statisticians, Friends or Foes?, in Mira J. and Sandoval F.(eds.), From Natural to Artificial Neural Computation, Proc. of International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain, Springer, pp. 454-461, 1995.
http://www.ofai.at/cgi-bin/tr-online?number+95-06

Flexer A.: Limitations of self-organizing maps for vector quantization and multi dimensional scaling, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1996.
http://www.ofai.at/cgi-bin/tr-online?number+96-23

Flexer A.: Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice, in Trappl R.(ed.), Cybernetics and Systems '96, Oesterreichische Studiengesellschaft fuer Kybernetik, Wien, pp.1005-1008, 1996.
http://www.ofai.at/cgi-bin/tr-online?number+95-16

Flexer A.: Limitations of Self-Organizing Maps for Vector Quantization and Multidimensional Scaling, in Mozer M.C., et al.(eds.), Advances in Neural Information Processing Systems 9 (NIPS'97), MIT Press/Bradford Books, Cambridge/London, pp.445-451, 1997.
http://www.ofai.at/cgi-bin/tr-online?number+96-23

Flexer A.: Spatio-temporal Clustering of Cognitive Evoked Potentials, Institut fuer Psychologie, Universitaet Wien, 1999.

Flexer A.: On the use of self-organizing maps for clustering and visualization, in Zytkow J.M. and Rauch J.(eds.), Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD'99, Prague, Czech Republic, Proceedings, p.80-88, 1999.
http://www.ofai.at/cgi-bin/tr-online?number+99-04

Flexer A.: Data mining and electroencephalography, Statistical Methods in Medical Research, 9: 395-413, 2000.
http://www.ofai.at/cgi-bin/tr-online?number+2000-12

Flexer A.: On the Use of Self-organizing Maps for Clustering and Visualization, Intelligent Data Analysis, Vol. 5, Number 5, pp. 373-384, 2001.
http://www.ofai.at/cgi-bin/tr-online?number+2001-34

Flexer A., Bauer H., Lamm C., Dorffner G.: Single Trial Estimation of Evoked Potentials Using Gaussian Mixture Models with Integrated Noise Component, in Dorffner G., et al.(eds.), Artificial Neural Networks - ICANN 2001, International Conference, Vienna, Austria, Lecture Notes In Computer Science 2130, Springer, pp. 609-616, 2001.
http://www.ofai.at/cgi-bin/tr-online?number+2001-05

Flexer A., Bauer H., Lamm C., Dorffner G.: Model-based Noise Reduction for Single Trial Evoked Potentials, in Miller D.J., et al.(eds.), Neural Networks for Signal Processing XI, Institute of Electrical and Electronics Engineers, Inc., New York, NY, pp.499-508, 2001.
http://www.ofai.at/cgi-bin/tr-online?number+2001-10

Flexer A., Bauer H., Pfripfl J., Dorffner G.: Using ICA for removal of ocular artifacts in EEG recorded from blind subjects, Neural Networks, Volume 18, Issue 7, pp. 998-1005, 2005.
http://www.ofai.at/cgi-bin/tr-online?number+2005-07

Flexer A., Dorffner G., Sykacek P., Rezek I.: An automatic, continuous and probabilistic sleep stager based on a hidden markov model, Applied Artificial Intelligence, Vol. 16, Num. 3, pp.199-207, 2002.
http://www.ofai.at/cgi-bin/tr-online?number+2001-35

Flexer A., Gruber G., Dorffner G.: Continuous Unsupervised Sleep Staging Based on a Single EEG Signal, in Dorronsoro J.R.(ed.), Artificial Neural Networks - ICANN 2002, Lecture Notes in Computer Science, Springer, pp. 1013-1018, 2002.
http://www.ofai.at/cgi-bin/tr-online?number+2002-03

Flexer A., Gruber G., Dorffner G.: Improvements on continuous unsupervised sleep staging, in Bourlard H., et al.(eds.), Neural Networks for Signal Processing XII, Institute of Electrical and Electronics Engineers, Inc., New York, NY, pp. 687-695, 2002.
http://www.ofai.at/cgi-bin/tr-online?number+2002-24

Flexer A., Gruber G., Dorffner G.: A reliable probabilistic sleep stager based on a single EEG signal., Artificial Intelligence in Medicine, 33(3)199-207, 2005.

Flexer A., Rappelsberger P., Dorffner G.: Discrimination of EEG Spectral Parameters Derived During Spatial Imagination Tests with Kohonen Networks (abstract), European Journal of Neuroscience, 1994.

Flexer A., Rappelsberger P., Dorffner G.: Processing of EEG coherence maps with topologically correct feature maps, in Eiselt M., et al.(eds.), Quantitative and Topological EEG and MEG Analysis, Universitaetsverlag Jena, 1995.

Flexer A., Sykacek P., Rezek I., Dorffner G.: Using Hidden Markov Models to build an automatic, continuous and probabilistic sleep stager, in Amari S.-I., et al.(eds.), Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IEEE Computer Society, Vol. III, 627-631, 2000.

Fritz G., Paletta L., Breithaupt R., Rome E., Dorffner G.: Learning predictive features in affordance-based robotics, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Szstems (IROS 2006), Beijing, China, October 9-15, pp.3642-3647, 2006.

Fritz G., Paletta L., Kumar M., Dorffner G., Breithaupt R., Rome E.: Visual learning of affordance-based cues, in Proceedings of the Ninth International Conference on the Simulation of Adaptive Behavior (SAB 2006), LNAI 4095, Springer Verlag, Berlin, pp.52-64, 2006.

Gruber G., Flexer A., Dorffner G.: Unsupervised continuous sleep analysis based on a single EEG channel (abstract), Journal of Sleep Research, 16th Congress of the European Sleep Research Society, Vol. 11, Supplement 1, p.89, 2002.

Gruber G., Flexer A., Dorffner G. Unsupervised continuous sleep analysis, Methods Find Exp Clin Pharmacol., 2002;24 Suppl D:51-6. PubMed PMID: 12575468.
View at MEDLINE

Hallas M., Dorffner G.: A Comparative Study on Feedforward and Recurrent Neural Networks in Time Series Prediction Using Gradient Descent Learning, in Trappl R.(ed.), Cybernetics and Systems '98 - Proc. of 14th European Meeting on Cybernetics and Systems Research, Austrian Society for Cybernetic Studies, Vienna, pp.644-647, 1998.

Hauser P.: Kontextfreie Sprachen und rekurrente neuronale Netze, Institut fuer Med.Kybernetik u. AI, Universitaet Wien, 1997.

Kloesch G., Kemp B., Penzel T., Schloegl A., Rappelsberger P., Trenker E., Gruber G., Zeitlhofer J., Saletu B., Herrmann W.M., Himanen S.-L., Kunz D., Barbanoj M., Roeschke J., Vaerri A., Dorffner G.: The SIESTA Project Polygraphic and Clinical Database, IEEE Engineering in Medicine and Biology Magazine, 20(3)51-57, 2001.

Lee A., Ulbricht C., Dorffner G.: Application of artificial neural networks for detection of abnormal fetal heart rate pattern: a comparison with conventional algorithms, Journal of Obstetrics and Gynaecology, 19(5)482-485, 1999.

Linhart G., Dorffner G.: A Self-Learning Visual Pattern Explorer and Recognizer using a Higher Order Neural Network, in IJCNN International Joint Conference on Neural Networks, Baltimore, IEEE, pp.705-710, 1992.
http://www.ofai.at/cgi-bin/tr-online?number+92-14

Linhart G., Dorffner G.: VieNet2 - Ein Simulationstool fuer Neuronale Netzwerke, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1993.

Linhart G., Dorffner G.: VieNet2 - Vienna Neural Network Toolkit 2: Ein Simulationstool fuer Neuronale Netzwerke in C, Version 2.0, Anwender-Handbuch, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1994.

Magdolen J., Rappelsberger P., Dorffner G., Flexer A., Winterer G.: Application of Non-Linear Classifiers to EEG at Rest for Identifying Psychiatric Disorders, in Trappl R.(ed.), Cybernetics and Systems '96, Oesterreichische Studiengesellschaft fuer Kybernetik, Wien, pp.592-596, 1996.

Magdolen J., Rappelsberger P., Dorffner G., Flexer A., Winterer G.: Artificial neural networks and data dimensionality reduction mappings for identification of psychiatric disorders in EEG, Medical and Biological Engineering and Computing, pp.237-238, 1996.

Magdolen J., Rappelsberger P., Dorffner G., Flexer A., Winterer G.: Artificial neural networks for discriminating relapsing from abstaining alcoholics, Proc. of 4th Int. Symposium on CNS Monitoring, (to appear), 1998.

Mannes C., Dorffner G.: Self-Organizing Detectors of Spatiotemporal Patterns, in Kindermann J. and Linden A.(eds.), Distributed Adaptive Neural Information Processing, Oldenbourg, Muenchen/Wien, pp. 89-102, 1990.

Mannes C., Dorffner G.: On Learning Content-Blind Rules, in Trappl R.(ed.), Cybernetics and Systems '90, World Scientific Publishing, Singapore/London, pp.1009-1016, 1990.

Miazhynskaia T., Dorffner G.: A Comparison of Bayesian Model Selection Based on MCMC with an Application to GARCH-type Models, Statistical Papers, 47, 525-549, 2006.

Miazhynskaia T., Dorffner G., Dockner E.J.: Risk management application of the recurrent mixture densitz network, Proceedings of Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP 2003), Lecture Notes in Computer Science, Springer Verlag Heidelberg, pp.589-596, 2003.

Miazhynskaia T., Fruehwirth-Schnatter S., Dorffner G.: Bayesian testing for non-linearity in volatility modeling, Computational Statistics & Data Analysis, 51(3)2029-2042, 2006.

Natter M., Feurstein M., Mild A., Taudes A., Trcka M., Dorffner G., Merz C.: Learning in the artificial factory, IEEE Proceedings of the Hawai`i International Conference on System Sciences (HICSS 2000), 2000.

Natter M., Mild A., Feurstein M., Dorffner G., Taudes A.: The Effect of Incentive Schemes and Organizational Arrangements on the New Product Development Process, Management Science, 47(8)1029-1045, 2001.

Neumann C., Gschwendtner M., Karnel F., Mair J., Dorffner G., Dorffner R.: Technical feasibility of the implantation of a monorail stent system into the renal arteries without pre-dilatation, Fortschr Roentgenstr (RoFo), 177:84-88, 2005.

Parfitt S., Dorffner G., Tino P.: Graded grammaticality in Prediction Fractal Machines, in Solla S.A., et al.(eds.), Advances in Neural Information Processing Systems 12, MIT Press, Cambridge/Boston/London, pp. 52-58, 2000.

Porenta G., Dorffner G., Kundrat S., Petta P., Duit J., Sochor H.: Automated Interpretation of Planar Thallium-201 Dipyridamole Stress-Redistribution Scintigrams using Artificial Neural Networks, Journal of Nuclear Medicine, 35(12)2041-2047, 1994.

Prem E.: A Description Framework for Solving the 'Theory Problem' in Connectionism - Analysis and a Proposal, Institut fuer Med.Kybernetik u. AI, Universitaet Wien, 1991.

Prem E.: Aspects of Rules and Connectionism, in Trappl R.(ed.), Cybernetics and Systems '92, World Scientific Publishing, Singapore/London, pp.1343-1350, 1992.

Prem E.: Die Anwendung neuronaler Netzwerke in der betrieblichen Unternehmung, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1993.

Prem E.: Symbol Grounding and Transcendental Logic, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+94-20

Prem E.: Symbol Grounding Revisited, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1994.
http://www.ofai.at/cgi-bin/tr-online?number+94-19

Prem E.: Connectionist Knowledge Transfer Methods, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1995.

Prem E.: Integrating Knowldge-Based Components into Neural Network Software, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1995.

Prem E.: Dynamic Symbol Grounding, State Construction, and the Problem of Teleology, in Mira J. and Sandoval F.(eds.), From Natural to Artificial Neural Computation, Proc. of International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain, Springer, 1995.

Prem E.: Symbol Grounding and Transcendental Logic, in Niklasson L. and Boden M.(eds.), Current Trends in Connectionism, Lawrence Erlbaum, New Haven/Hillsdale/Hove, p. 271-282, 1995.

Prem E.: Understanding Complex Systems: What Can the Speaking Lion Tell Us?, in Steels L.(ed.), The Biology and Technology of Autonomous Agents, Springer, Berlin/Heidelberg/New York/Tokyo, 1995.

Prem E.: New AI: Naturalness Revealed in the Study of Artificial Intelligence, in Dorffner G.(ed.), Neural Networks and a New AI, International Thomson Computer Press, London, pp. 13-28, 1996.

Prem E., Dorffner G.: Effektive Nutzung von a priori Wissen fuer neuronale Netze - Europaeische Forschung im Rahmen von ESPRIT-II, Informations- und Kommunikationstechnologie fuer das neue Europa (Wiener IT-Kongress), ADV, Wien, 1993.

Prem E., Hoertnagl E., Dorffner G.: Growing Event Memories for Autonomous Robots, Simulation of Adaptive Behavior, Workshop on Growing up Artifacts that Live, 2002.

Prem E., Mackinger M., Dorffner G., Porenta G., Sochor H.: Concept Support as a Method for Programming Neural Networks with Symbolic Knowledge, in Ohlbach H.J.(ed.), GWAI-92: Advances in Artificial Intelligence, Springer, Berlin/Heidelberg/New York/Tokyo, 1993.
http://www.ofai.at/cgi-bin/tr-online?number+92-04

Rappelsberger P., Dorffner G., Flexer A.: Classification of EEG Coherence Maps of Cognitive Processes (Abstract), Journal of Clinical Monitoring, 1993.

Rappelsberger P., Dorffner G., Flexer A.: Classification of EEG Coherence Maps of Cognitive Processes, in Loeffler W.H., et al.(eds.), ZNS-Monitoring, Verlag Wilhelm Maudrich, Wien-Muenchen-Bern, pp.375-388, 1993.

Rappelsberger P., Dorffner G., Flexer A.: Kuenstliche Neuronale Netzwerke zur Klassifikation von EEG-Kohaerenz Maps: Beispiele aus psychophysiologischen Studien, Biomed. Technik, Band 38, Ergaenzungsband, 1993.

Rappelsberger P., Magdolen J., Winterer G., Dorffner G., Flexer A.: Classification of EEG of schizophrenics and depressives with artificial neural networks, Proc. of 4th Int. Symposium on CNS Monitoring, (to appear), 1998.

Rappelsberger P., Trenker E., Rothmann C., Gruber G., Sykacek P., Roberts S., Kloesch G., Zeitlhofer J., Anderer P., Saletu B., Schloegl A., Vaerri A., Kemp B., Penzel T., Herrmann W.M., Hasan J., Barbanoj M., Roeschke J., Kunz D., Dorffner G.: Das Projekt SIESTA, Klinische Neurophysiologie, 32, 76-88, 2001.

Rome E., Hertzberg J., Dorffner G., Doherty P. (eds.): Towards Affordance-Based Robot Control, Springer Berlin/Heidelberg, 2008.

Rosipal R., Dorffner G., Trenker E.: Can ICA improve sleep-spindles detection?, Neural Network World, 8(5), 539-547, 1998.

Rothmann C.: Validierung eines automatischen Sleep Analysers, Institut fuer Medizinische Kybernetik, Universitaet Wien, 2001.

Rotter M., Dorffner G.: Struktur und Konzeptrelationen in verteilten Netzwerken, in Dorffner G.(ed.), Konnektionismus in Artificial Intelligence und Kognitionsforschung, Springer, Berlin/Heidelberg/New York/Tokyo, pp.85-94, 1990.

Sallans B., Dorffner G., Karatzoglou A.: Feedback Effects in Interacting Markets, Proceedings of the Third Workshop on Agent-Based Simulation, SCS-European Publishing House, Ghent, Belgium, pp.126-131, 2002.

Sallans B., Pfister A., Karatzoglou A., Dorffner G.: Simulation and validation of an integrated markets model, Journal of Artificial Societies and Social Simulation, 6(4), 2003.

Schellner K., Dorffner G., Prem E.: Predicting Rainfall Patterns Using Regularized Gaussian Mixture Models, in Trappl R.(ed.), Cybernetics and Systems 2000, Oesterreichische Studiengesellschaft fuer Kybernetik, Wien, pp.564-569, 2000.

Schittenkopf C., Dorffner G.: Risk-neutral extraction from option prices: improved pricing with mixture density networks, IEEE Transactions on Neural Networks, 12(4)716-725, 2001.
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Schittenkopf C., Dorffner G., Dockner E.J.: Identifying Stochastic Processes with Mixture Density Networks, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1998.
http://www.ofai.at/cgi-bin/tr-online?number+98-16

Schittenkopf C., Dorffner G., Dockner E.J.: Forecasting time-dependent conditional densities: A semi-nonparametric neural network approach, Journal of Forecasting, 19, 355-374, 2000.
http://www.ofai.at/cgi-bin/tr-online?number+99-14

Schittenkopf C., Dorffner G., Dockner E.J.: On non-linear, stochastic dynamics in economic and financial time series, Studies in Nonlinear Dynamics and Econometrics, 4(3), 2000.

Schittenkopf C., Tino P., Dorffner G.: The benefit of information reduction for trading strategies, Applied Economics, 34(7)917-930, 2002.
http://www.ofai.at/cgi-bin/tr-online?number+2000-21

Schloegl A., Kemp B., Penzel T., Kunz D., Himanen S.-L., Varri A., Dorffner G., Pfurtscheller G.: Quality Control of polysomnographic sleep data by histogram and entropy analysis, Clinical Neurophysiology, 110(12)2165-2170, 1999.

Stoecklmayer C., Dorffner G., Schmidt C., Schima H.: An Artificial Neural Network Based Noninvasive Detector for Suction and Left Atrium Pressure in the Control of Rotary Blood Pumps - An In-vitro Study, Artificial Organs, 19(7)719-724, 1995.

Sykacek P.: Equivalent Error Bars for Neural Network Classifiers Trained by Bayesian Inference, in Verleysen M.(ed.), Proceedings of the 5th European Symposium on Artificial Neural Networks, April 16-18, Bruges, Belgium, D-Facto Publishers, Brussels, pp.121-126, 1997.

Sykacek P.: Outliers and Bayesian Inference, in Heiss M. (ed.), Proceedings of the International ICSC/IFAC Symposion on Neural Computation (NC 1998), Vienna, Austria., pp. 973-978, 1998.

Sykacek P., Dorffner G.: Biosignalverarbeitung mit Bayes'schen Methoden, OeGAI Journal, 18/3, 18-24, 1999.

Sykacek P., Dorffner G., Filz O., Rappelsberger P., Zeitlhofer J.: Evaluierung einer automatischen Schlafstadien-Klassifizierung mittels nichtlinearer neuraler Netze (abstract), in Medizinische Experten- und wissensbasierte Systeme am AKH Wien, Institut f. Med.Computerwissenschaften und Institut f. Med.Kybernetik und Artificial Intelligence, Universitaet Wien, 1996.

Sykacek P., Dorffner G., Filz O., Rappelsberger P., Zeitlhofer J.: Classification of rem sleep periods with artificial neural networks, Proc. of Measurement '97, Smolenice 1997, 1997.

Sykacek P., Dorffner G., Rappelsberger P., Zeitlhofer J.: Evaluating confidence measures in a neural network based sleep stager, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 1997.

Sykacek P., Dorffner G., Rappelsberger P., Zeitlhofer J.: Experiences with bayesian learning in a real world application, in Jordan M.I., et al., Advances in Neural Information Processing Systems 10 (NIPS'98), MIT Press/Bradford Books, Cambridge/London, pp.964-970, 1998.

Sykacek P., Dorffner G., Rappelsberger P., Zeitlhofer J.: Relevant input features for sleep staging with neural networks, Proc. of 4th Int. Symposium on CNS Monitoring, Gmunden, 1996, 1998.

Sykacek P., Dorffner G., Rappelsberger P., Zeitlhofer J.: Improving biosignal processing through modeling uncertainty: Bayes vs. non-Bayes in sleep staging, Applied Artificial Intelligence, 16(5)395-421, 2002.

Sykacek P., Roberts S., Rezek I., Flexer A., Dorffner G.: A Probabilistic Approach to High-Resolution Sleep Analysis, in Dorffner G., et al.(eds.), Artificial Neural Networks - ICANN 2001, International Conference, Vienna, Austria, Lecture Notes In Computer Science 2130, Springer, pp. 617-624, 2001.
http://www.robots.ox.ac.uk/~sjrob/Pubs/sleep_icann01.ps.gz

Sykacek P., Roberts S.J., Rezek I., Flexer A., Dorffner G.: Classification in the sampling paradigm: A predictive approach towards a SIESTA sleep analyzer, Medical and Biological Engineering and Computing, Supplement 2, Proceedings of EMBEC'99, 1999.

Sykacek P., Roberts S.J., Rezek I.A., Flexer A., Dorffner G.: Bayesian wrappers versus conventional filters: Feature subset selection in the SIESTA project, Medical and Biological Engineering and Computing, Supplement 2, Proceedings of EMBEC '99, 1999.

Sykacek P., Roberts S.J., Rezek I.A., Flexer A., Dorffner G.: Reliability in preprocessing - Bayes rules SIESTA, Medical and Biological Engineering and Computing, Supplement 2, Proceedings of EMBEC '99, November 4-7, Vienna, Austria, 1999.

Taudes A., Dorffner G., Natter M., Feurstein M., Merz C., Mild A., Trcka M.: Learning Market-Production Interaction, Proceedings of The 1999 IEEE Systems, Man, and Cybernetics Conference (SMC'99) , Tokyo 1999, 2000.

Tino P., Dorffner G.: Recurrent Neural Networks with Iterated Function Systems Dynamics, in NC'98, Proceedings of the ICSC/IFAC Symposium on Neural Computation, Vienna, Austria., pp.526-532, 1998.
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Tino P., Dorffner G.: Building predictive models from spatial representations of symbolic sequences, in Solla S.A., et al.(eds.), Advances in Neural Information Processing Systems 12, MIT Press, Cambridge/Boston/London, pp. 645-651, 2000.

Tino P., Dorffner G.: Predicting the future of discrete sequences from fractal representations of the past, Machine Learning, 45(2)187-217, 2001.

Tino P., Dorffner G., Schittenkopf C.: Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics, In Hybrid Neural Symbolic Integration, (eds) S. Wermter, R. Sun. Springer Verlag, 2000, pp.255-269, 2000.
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Tino P., Schittenkopf C., Dorffner G.: Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams: Lessons learned from financial volatility trading, Pattern Analysis and Applications, 4:283-299, 2001.
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Tino P., Schittenkopf C., Dorffner G.: Financial volatility trading using recurrent neural networks, IEEE Transactions on Neural Networks, 12(4)865-874, 2001.

Trenker E., Hajek J., Rappelsberger P., Zeitlhofer J., Anderer P., Dorffner G.: Automatic detection of sleep spindles using artificial neural networks (abstract), Journal of Sleep Research, 7, suppl. 2, 280, 1998.

Trenker E., Rappelsberger P., Hajek J., Zeitlhofer J., Dorffner G.: Automatische Erkennung von Schlafspindeln, Biomed. Technik, 43/2, 34-36, 1998.

Ulbricht C.: Handling Sequences with a Competitive Recurrent Network, Proc. of International Joint Conference on Neural Networks (IJCNN'92), Baltimore, 1992.

Ulbricht C.: Handling Sequences with a Competitive Recurrent Network, in IJCNN International Joint Conference on Neural Networks, Baltimore, IEEE, pp.731-736, 1992.

Ulbricht C.: Multi-Recurrent Networks for Traffic Forecasting, in Proceedings of the 12th National Conference on Artificial Intelligence, AAAI Press/MIT Press, Cambridge/Menlo Park, pp.883-888, 1994.

Ulbricht C.: State Formation in Neural Networks for Handling Temporal Information, Institut fuer Med.Kybernetik u. AI, Universitaet Wien, 1995.

Ulbricht C.: Handling Time-Warped Sequences with Neural Networks, in Maes P., et al.(eds.), From Animals to Animats 4, MIT Press/Bradford Books, Cambridge/London, pp.180-192, 1996.
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Ulbricht C., Dorffner G., Canu S., Guillemyn D., Marijuan G., Olarte J., Rodriguez C., Martin I.: Mechanisms for Handling Sequences with Neural Networks, in Dagli C.H., et al.(eds.), Intelligent Engineering Systems through Artificial Neural Networks, Volume 2, ASME Press, New York, 1992.
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Ulbricht C., Dorffner G., Lee A.: Computer Supported Evaluation of the CTG with Neural Networks (abstract), in Pedotti A. and Rabischong P.(eds.), 3rd European Conference on Engineering and Medicine, European Society for Engineering and Medicine, p. 435, 1995.

Ulbricht C., Dorffner G., Lee A.: Forecasting Fetal Heartbeats with Neural Networks, in Bulsari A.B., et al.(eds.), Solving Engineering Problems with Neural Networks, Systeemitekniikan seura ry, Turku, pp.403-406, 1996.
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Ulbricht C., Dorffner G., Lee A.: CTG-Analyse mit neuronalen Netzen, in Medizinische Experten- und wissensbasierte Systeme am AKH Wien, Institut f. Med.Computerwissenschaften und Institut f. Med.Kybernetik und Artificial Intelligence, Universitaet Wien, 1996.

Ulbricht C., Dorffner G., Lee A.: Neural Networks for Recognizing Patterns in Cardiotocograms, Artificial Intelligence in Medicine,, 271-284, 1998.
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Veits W., Dorffner G., Schmidt C., Schima H.: On-line adaptive artificial neural network-based monitoring of rotary blood pumps, in Bulsari A.B., et al.(eds.), Solving Engineering Problems with Neural Networks, Systeemitekniikan seura ry, Turku, pp.399-402, 1996.

Wiklicky H.: Graphical Design and Description of Neural Networks, in IJCNN International Joint Conference on Neural Networks, Baltimore, IEEE, pp.459-464, 1992.

Wiklicky H.: On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks, in Cowan J.D., et al.(eds.), Advances in Neural Information Processing Systems 6 (NIPS'94), Morgan Kaufmann, Los Altos/Palo Alto/San Francisco, pp.431-436, 1994.

Wiklicky H., Denoeux T., Lafuente A., Olarte J., Rementeria S., Walravens V.: A Tripartite Framework for Artificial Neural Networks, in Aleksander I. and Taylor J.(eds.), Artificial Neural Networks, 2, North-Holland, Amsterdam/New York, pp.1031-1034, 1992.