Prof. Dr. rer. nat. Bernhard Sick

Fachgebietsleiter; Teamleiter: Collaborative Interactive Learning (CIL); Teamleiter: AI for Computationally Intelligent Systems (AI4CIS)

Sick, Bernhard
Telefon
+49 561 804-6020
Fax
+49 561 804-6022
E-Mail
Standort
Wilhelmshöher Allee 73
34121 Kassel
Raum
WA-altes Gebäude (WA 73), ohne Raumangabe

Aufgabenbereich

[Aufgabenbereich Platzhalter]

Pu­bli­ka­tio­nen

[ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ] [ 2013 ] [ 2012 ] [ 2011 ] [ 2010 ] [ 2007 ] [ 2006 ]

2021 [ nach oben ]

  • Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks. Heidecker, Florian; Hannan, Abdul; Bieshaar, Maarten; Sick, Bernhard (2021). 361–374.
     
  • Toward optimal probabilistic active learning using a Bayesian approach. Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard in Machine Learning (2021).
     
  • Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. He, Yujiang; Huang, Zhixin; Sick, Bernhard (2021).
     
  • Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. Huseljic, Denis; Sick, Bernhard; Herde, Marek; Kottke, Daniel (2021).
     
  • Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. Kress, Viktor; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard (2021). 2723–2730.
     
  • Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders. Möller, Felix; Botache, Diego; Huseljic, Denis; Heidecker, Florian; Bieshaar, Maarten; Sick, Bernhard (2021).
     
  • Novelty detection in continuously changing environments. Gruhl, Christian; Sick, Bernhard; Tomforde, Sven in Future Generation Computer Systems (2021). 114 138–154.
     
  • Novelty based Driver Identification on RR Intervals from ECG Data. Heidecker, Florian; Gruhl, Christian; Sick, Bernhard (2021). 407–421.
     
  • Multi-annotator Probabilistic Active Learning. Herde, Marek; Kottke, Daniel; Huseljic, Denis; Sick, Bernhard (2021).
     
  • Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard (2021).
     
  • Intelligent and Interactive Video Annotation for Instance Segmentation using Siamese Neural Networks. Schneegans, Jan; Bieshaar, Maarten; Heidecker, Florian; Sick, Bernhard (2021). 375–389.
     
  • Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution. Zernetsch, Stefan; Schreck, Steven; Kress, Viktor; Doll, Konrad; Sick, Bernhard (2021).
     
  • Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. Schreiber, Jens; Sick, Bernhard (2021).
     
  • AdaPT: Adaptable particle tracking for spherical microparticles in lab on chip systems. Dingel, Kristina; Huhnstock, Rico; Knie, André; Ehresmann, Arno; Sick, Bernhard in Computer Physics Communications (2021). 262 107859.
     

2020 [ nach oben ]

  • Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. Kottke, Daniel; Herde, Marek; Sandrock, Christoph; Huseljic, Denis; Krempl, Georg; Sick, Bernhard in arXiv e-prints (2020). arXiv:2006.01732.
     
  • Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar. Scheiner, Nicolas; Kraus, Florian; Wei, Fangyin; Phan, Buu; Mannan, Fahim; Appenrodt, Nils; Ritter, Werner; Dickmann, Jurgen; Dietmayer, Klaus; Sick, Bernhard; Heide, Felix (2020).
     
  • Representation Learning in Power Time Series Forecasting. Henze, Janosch; Schreiber, Jens; Sick, Bernhard W. Pedrycz, S.-M. Chen (reds.) (2020). 67–101.
     
  • Reconstruction of offsets of an electron gun using deep learning and an optimization algorithm. Meier, David; Hartmann, Gregor; Völker, Jens; Viefhaus, Jens; Sick, Bernhard O. Chubar, K. Sawhney (reds.) (2020). (Vol. 11493) 71–77.
     
  • Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets. Bieshaar, Maarten; Schreiber, Jens; Vogt, Stephan; Gensler, André; Sick, Bernhard in arXiv e-prints (2020). arXiv:2010.05898.
     
  • Probabilistic upscaling and aggregation of wind power forecasts. Henze, Janosch; Siefert, Malte; Bremicker-Trübelhorn, Sascha; Asanalieva, Nazgul; Sick, Bernhard in Energy, Sustainability and Society (2020). 10(1) 15.
     
  • Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks. Kress, V.; Schreck, S.; Zernetsch, S.; Doll, K.; Sick, B. (2020). 2723–2730.
     
  • Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?. Scheiner, Nicolas; Schumann, Ole; Kraus, Florian; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2020). 1–8.
     
  • Normal-Wishart clustering for novelty detection. Gruhl, Christian; Schmeißing, Jörn; Tomforde, Sven; Sick, Bernhard (2020). 64–69.
     
  • Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning. Haase-Schütz, Christian; Stal, Rainer; Hertlein, Heinz; Sick, Bernhard in arXiv e-prints (2020). arXiv:2002.02705.
     
  • Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. Pham Minh, T.; Kottke, D.; Tsarenko, A.; Gruhl, C.; Sick, B. (2020).
     
  • Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. He, Y.; Henze, J.; Sick, B. (2020).
     
  • Extended Coopetitive Soft Gating Ensemble. Deist, Stephan; Schreiber, Jens; Bieshaar, Maarten; Sick, Bernhard in arXiv e-prints (2020). arXiv:2004.14026.
     
  • Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. Englhardt, Adrian; Trittenbach, Holger; Kottke, Daniel; Sick, Bernhard; Böhm, Klemens in arXiv e-prints (2020). arXiv:2009.13853.
     
  • Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. He, Yujiang; Henze, Janosch; Sick, Bernhard (2020). (Vol. 53) 12175–12182.
     
  • A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Tomforde, Sven; Gruhl, Christian; Sick, Bernhard (2020). 166–169.
     

2019 [ nach oben ]

  • Wind Power Forecasting Based on Deep Neural Networks and Transfer Learning. Vogt, Stephan; Braun, Axel; Dobschinski, Jan; Sick, Bernhard U. Betancourt, T. Ackermann (reds.) (2019).
     
  • Using grid supporting flexibility in electricity distribution networks. König, Immanuel; Heilmann, Erik; Henze, Janosch; David, Klaus; Wetzel, Heike; Sick, Bernhard K. David, K. Geihs, M. Lange, G. Stumme (reds.) (2019). 531–544.
     
  • Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks. Zernetsch, S.; Reichert, H.; Kress, V.; Doll, K.; Sick, B. (2019). 810–815.
     
  • Towards Corner Case Identification in Cyclists’ Trajectories. Heidecker, F.; Bieshaar, M.; Sick, B. (2019).
     
  • Start Intention Detection of Cyclists using an LSTM Network. Kress, Viktor; Jung, Janis; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard C. Draude, M. Lange, B. Sick (reds.) (2019). 219–228.
     
  • Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 642–649.
     
  • Pose Based Trajectory Forecast of Vulnerable Road Users. Kress, V.; Zernetsch, S.; Doll, K.; Sick, B. (2019).
     
  • Pose Based Start Intention Detection of Cyclists. Kress, V.; Jung, J.; Zernetsch, S.; Doll, K.; Sick, B. (2019). 2381–2386.
     
  • Limitations of Assessing Active Learning Performance at Runtime. Kottke, Daniel; Schellinger, Jim; Huseljic, Denis; Sick, Bernhard in arXiv e-prints (2019). arXiv:1901.10338.
     
  • Intentions of Vulnerable Road Users -- Detection and Forecasting by Means of Machine Learning. Goldhammer, M.; Köhler, S.; Zernetsch, S.; Doll, K.; Sick, B.; Dietmayer, K. in IEEE Transactions on Intelligent Transportation Systems (2019). 1–11.
     
  • Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. Schreiber, Jens; Buschin, Artjom; Sick, Bernhard K. David, K. Geihs, M. Lange, G. Stumme (reds.) (2019). 585–598.
     
  • Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. Schreiber, Jens; Jessulat, Maik; Sick, Bernhard I. V. Tetko, V. Krurková, P. Karpov, F. Theis (reds.) (2019). 550–564.
     
  • Explicit Consideration of Resilience in Organic Computing Design Processes. Tomforde, S.; Gelhausen, P.; Gruhl, C.; Haering, I.; Sick, B. (2019). 1–6.
     
  • Early Pedestrian Movement Detection Using Smart Devices Based on Human Activity Recognition. Botache, Diego; Dandan, Liu; Bieshaar, Maarten; Sick, Bernhard C. Draude, M. Lange, B. Sick (reds.) (2019). 229–238.
     
  • Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. Sandrock, C.; Herde, M.; Calma, A.; Kottke, D.; Sick, B. (2019). 1–8.
     
  • Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields. Hanika, Tom; Herde, Marek; Kuhn, Jochen; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiß, Sarah; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Tomforde, Sven; Zweig, Katharina Anna in arXiv e-prints (2019). arXiv:1905.07264.
     
  • Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 5–9.
     
  • Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices. Scheiner, Nicolas; Haag, Stefan; Appenrodt, Nils; Duraisamy, Bharanidhar; Dickmann, Jürgen; Fritzsche, Martin; Sick, Bernhard (2019). 1–10.
     
  • A Multi-Stage Clustering Framework for Automotive Radar Data. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2019). 2060–2067.
     

2018 [ nach oben ]

  • Towards Proactive Health-enabling Living Environments : Simulation-based Study and Research Challenges. Tomforde, Sven; Dehling, Tobias; Haux, Reinhold; Huseljic, Denis; Kottke, Daniel; Scheerbaum, Jonas; Sick, Bernhard; Sunyaev, Ali; Wolf, Klaus-Hendrik (2018). 1–8.
     
  • Towards Cooperative Self-adapting Activity Recognition. Jahn, Andreas; Tomforde, Sven; Morold, Michel; David, Klaus; Sick, Bernhard (2018). 215–222.
     
  • The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning. Kottke, Daniel; Calma, Adrian; Huseljic, Denis; Sandrock, Christoph; Kachergis, George; Sick, Bernhard (2018).
     
  • Starting Movement Detection of Cyclists Using Smart Devices. Bieshaar, M.; Depping, M.; Schneegans, J.; Sick, B. (2018).
     
  • Smart Device Stealing and CANDIES. Jänicke, Martin; Schmidt, Viktor; Sick, Bernhard; Tomforde, Sven; Lukowicz, Paul; Schmeißing, Jörn (2018). 247–273.
     
  • Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data. Calma, A.; Reitmaier, T.; Sick, B. in Information Sciences (2018). 456 13–33.
     
  • Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models. Jänicke, Martin; Sick, Bernhard; Tomforde, Sven in Informatics (2018). 5(3) 38.
     
  • Security Issues in Self-Improving System Integration - Challenges and Solution Strategies. Heck, Henner; Sick, Bernhard; Tomforde, Sven (2018). 176–181.
     
  • Sampling Strategies for Representative Time Series in Load Flow Calculations. Henze, Janosch; Kutzner, Stephan; Sick, Bernhard (2018). 27–48.
     
  • Radar-based Feature Design and Multiclass Classification for Road User Recognition. Scheiner, Nicolas; Appenrodt, Nils; Dickmann, Jürgen; Sick, Bernhard (2018). 779–786.
     
  • Quantifying the Influences on Probabilistic Wind Power Forecasts. Schreiber, Jens; Sick, Bernhard (2018). (Vol. 3) 6.
     
  • Novelty detection with CANDIES: a holistic technique based on probabilistic models. Gruhl, Christian; Sick, Bernhard in International Journal of Machine Learning & Cybernetics (2018). 9(6) 927–945.
     
  • Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration. Calma, Adrian; Oeste-Reiß, Sarah; Sick, Bernhard; Leimeister, Jan Marco (2018).
     
  • Human Pose Estimation in Real Traffic Scenes. Kress, V.; Jung, J.; Zernetsch, S.; Doll, K.; Sick, B. (2018).
     
  • Hosting capacity of low-voltage grids for distributed generation: Classification by means of machine learning techniques. Breker, Sebastian; Rentmeister, Jan; Sick, Bernhard; Braun, Martin in Applied Soft Computing (2018). 70 195–207.
     
  • Hijacked Smart Devices -- Methodical Foundations for Autonomous Theft Awareness based on Activity Recognition and Novelty Detection. Jänicke, Martin; Schmidt, Viktor; Sick, Bernhard; Tomforde, Sven; Lukowicz, Paul (2018).
     
  • Gestaltungsraum für proactive Smart Homes zur Gesundheitsförderung. Kromat, Theresa; Dehling, Tobias; Haux, Reinhold; Peters, Christoph; Sick, Bernhard; Tomforde, Sven; Wolf, Klaus-Hendrik; Sunyaev, Ali (2018).
     
  • Generalizing Application Agnostic Remaining Useful Life Estimation Using Data-Driven Open Source Algorithms. Schlegel, B.; Mrowca, A.; Wolf, P.; Sick, B.; Steinhorst, S. (2018).
     
  • Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network. Zernetsch, Stefan; Kress, Viktor; Sick, Bernhard; Doll, Konrad (2018). 1–6.
     
  • Coopetitive Soft Gating Ensemble. Deist, S.; Bieshaar, M.; Schreiber, J.; Gensler, A.; Sick, B. (2018).
     
  • Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure. Reitberger, G.; Zernetsch, S.; Bieshaar, M.; Sick, B.; Doll, K.; Fuchs, E. (2018).
     
  • Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble. Bieshaar, M.; Zernetsch, S.; Hubert, A.; Sick, B.; Doll, K. in IEEE Transactions on Intelligent Vehicles (2018). 3(4)
     
  • Comparing the Effects of Disturbances in Self-adaptive Systems - A Generalised Approach for the Quantification of Robustness. Tomforde, Sven; Kantert, Jan; Müller-Schloer, Christian; Bödelt, Sebastian; Sick, Bernhard in Transactions on Computational Collective Intelligence (2018). 28 193–220.
     
  • Collaborative Interactive Learning. Sick, Bernhard; Oeste-Reiß, Sarah; Schmidt, Albrecht; Tomforde, Sven; Zweig, Katharina Anna in Informatik Spektrum (2018). 41(1) 52–55.
     
  • Automated Active Learning with a Robot. Scharei, Kristina; Herde, Marek; Bieshaar, Maarten; Calma, Adrian; Kottke, Daniel; Sick, Bernhard in Archives of Data Science, Series A (Online First) (2018). 5(1) A16, 15 S. online.
     
  • Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime. Gruhl, Christian; Tomforde, Sven; Sick, Bernhard (2018). 198–203.
     
  • Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques. Herde, Marek; Kottke, Daniel; Calma, Adrian; Bieshaar, Maarten; Deist, Stephan; Sick, Bernhard (2018).
     
  • Active Learning with Realistic Data -- A Case Study. Calma, Adrian; Stolz, Moritz; Kottke, Daniel; Tomforde, Sven; Sick, Bernhard (2018).
     
  • A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies. Gensler, André; Sick, Bernhard; Vogt, Stephan in Renewable and Sustainable Energy Reviews (2018). 96 352–379.
     
  • A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation. Gensler, André; Sick, Bernhard in arXiv e-prints (2018). arXiv:1803.06344.
     
  • A Concept for Productivity Tracking based on Collaborative Interactive Learning Techniques. Calma, Adrian; Kuhn, Jochen; Leimeister, Jan Marco; Lukowicz, Paul; Oeste-Reiss, Sarah; Schmidt, Albrecht; Sick, Bernhard; Stumme, Gerd; Tomforde, Sven; Zweig, Anna Katharina (2018). 150–159.
     

2017 [ nach oben ]

  • Simulation of Annotators for Active Learning: Uncertain Oracles. Calma, Adrian; Sick, Bernhard in CEUR Workshop Proceedings (2017). (Vol. 1924) 49–58.
     
  • Self-Adaptation of Activity Recognition Systems to New Sensors. Bannach, David; Jänicke, Martin; Rey, Vitor F.; Tomforde, Sven; Sick, Bernhard; Lukowicz, Paul in arXiv e-prints (2017). arXiv:1701.08528.
     
  • Quantitative Robustness -- A Generalised Approach to Compare the Impact of Disturbances in Self-organising Systems. Kantert, J.; Tomforde, S.; Müller-Schloer, C.; Edenhofer, S.; Sick, B. (2017). (Vol. 1) 39–50.
     
  • Probabilistic wind power forecasting: A multi-scheme ensemble technique with gradual coopetitive soft gating. Gensler, A.; Sick, B. (2017). 1–10.
     
  • Probabilistic Active Learning with Structure-Sensitive Kernels. Lang, Dominik; Kottke, Daniel; Krempl, Georg; Sick, Bernhard in CEUR Workshop Proceedings (2017). (Vol. 1924) 37–48.
     
  • Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria. Gensler, A.; Sick, B. in Pattern Analysis and Applications (2017). 1–20.
     
  • Organic Computing in the Spotlight. Tomforde, Sven; Sick, Bernhard; Müller-Schloer, Christian in arXiv e-prints (2017). arXiv:1701.08125.
     
  • On Methodological and Technological Challenges for Proactive Health Management in Smart Homes. Wolf, J.-H.; Dehling, T.; Haux, R.; Sick, B.; Sunyaev, A.; Tomforde, S. J. Mantas, A. Hasman, P. Gallos, M. S. Househ (reds.) (2017). 209–212.
     
  • Measuring Self Organisation at Runtime -- A Quantification Method based on Divergence Measures. Tomforde, S.; Kantert, J.; Sick, B. (2017). (Vol. 1) 96–106.
     
  • Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. Calma, A.; Kottke, D.; Sick, B.; Tomforde, S. (2017). 109–116.
     
  • Interactive Learning Without Ground Truth. Würtz, Rolf P.; Tomforde, Sven; Calma, Adrian; Kottke, Daniel; Sick, Bernhard in Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.) (2017). (Vol. 11) 1–4.
     
  • Identifying Representative Load Time Series for Load Flow Calculations. Henze, Janosch; Kneiske, Tanja; Braun, Martin; Sick, Bernhard (2017). 83–93.
     
  • Identifying Representative Load Time Series for Load Flow Calculations. Henze, Janosch; Kneiske, Tanja; Braun, Martin; Sick, Bernhard W. L. Woon, Z. Aung, O. Kramer, S. Madnick (reds.) (2017). 83–93.
     
  • Highly Automated Learning for Improved Active Safety of Vulnerable Road Users. Bieshaar, M.; Reitberger, G.; Kress, V.; Zernetsch, S.; Doll, K.; Fuchs, E.; Sick, B. (2017).
     
  • Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence. Bieshaar, M.; Reitberger, G.; Zernetsch, S.; Sick, B.; Fuchs, E.; Doll, K. (2017). 67–87.
     
  • Dealing with class imbalance the scalable way: Evaluation of various techniques based on classification grade and computational complexity. Schlegel, Bernhard; Sick, Bernhard (2017). 69–78.
     
  • Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure. Bieshaar, M.; Zernetsch, S.; Depping, M.; Sick, B.; Doll, K. (2017).
     
  • Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Kottke, Daniel; Calma, Adrian; Huseljic, Denis; Krempl, Georg; Sick, Bernhard in CEUR Workshop Proceedings (2017). (Vol. 1924) 2–14.
     
  • A Concept for Intelligent Collaborative Network Intrusion Detection. Gruhl, C.; Beer, F.; Heck, H.; Sick, B.; Bühler, U.; Wacker, A.; Tomforde, S. (2017).
     

2016 [ nach oben ]

  • Trajectory Prediction of Cyclists Using a Physical Model and an Artificial Neural Network. Zernetsch, S.; Kohnen, S.; Goldhammer, M.; Doll, K.; Sick, B. (2016). 833–838.
     
  • Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptions. Goldhammer, M.; Köhler, S.; Doll, K.; Sick, B. in Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015, Y. Bi, S. Kapoor, R. Bhatia (reds.) (2016). (Vol. 650) 259–279.
     
  • Towards Self-Improving Activity Recognition Systems based on Probabilistic, Generative Models. Jänicke, M.; Tomforde, S.; Sick, B. (2016). 285–291.
     
  • Towards Autonomous Self-tests at Runtime. Heck, H.; Wacker, A.; Rudolph, S.; Gruhl, C.; Sick, B.; Tomforde, S. (2016). 98–99.
     
  • Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers. Fisch, D.; Gruhl, C.; Kalkowski, E.; Sick, B.; Ovaska, S. J. in Information Sciences (2016). 370--371 476–496.
     
  • Semi-Supervised Active Learning for Support Vector Machines: A Novel Approach that Exploits Structure Information in Data. Reitmaier, Tobias; Calma, Adrian; Sick, Bernhard in arXiv e-prints (2016). arXiv:1610.03995.
     
  • Resp-kNN: A probabilistic k-nearest neighbor classifier for sparsely labeled data. Calma, A.; Reitmaier, T.; Sick, B. (2016). 4040–4047.
     
  • Probabilistic Active Learning for Active Class Selection. Kottke, D.; Krempl, G.; Stecklina, M.; Styp von Rekowski, C.; Sabsch, T.; Pham Minh, T.; Deliano, M.; Spiliopoulou, M.; Sick, B. K. Mathewson, K. Subramanian, R. Loftin (reds.) (2016). 1–9.
     
  • Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. Heck, H.; Gruhl, C.; Rudolph, S.; Wacker, A.; Sick, B.; Hähner, J. (2016). 1–8.
     
  • Lifelong Learning and Collaboration of Smart Technical Systems in Open-Ended Environments -- Opportunistic Collaborative Interactive Learning. Bahle, G.; Calma, A.; Leimeister, J. M.; Lukowicz, P.; Oeste-Reiss, S.; Reitmaier, T.; Schmidt, A.; Sick, B.; Stumme, G.; Zweig, K. A. (2016). 1–10.
     
  • Generative Exponential Smoothing and Generative ARMA Models to Forecast Time-Variant Rates or Probabilities. Kalkowski, E.; Sick, B. in Time Series Analysis and Forecasting: Selected Contributions from the ITISE Conference, I. Rojas, H. Pomares (reds.) (2016). 75–88.
     
  • From Active Learning to Dedicated Collaborative Interactive Learning. Calma, A.; Leimeister, J. M.; Lukowicz, P.; Oeste-Reiß, S.; Reitmaier, T.; Schmidt, A.; Sick, B.; Stumme, G.; Zweig, K. A. (2016). 1–8.
     
  • Forecasting Wind Power -- An Ensemble Technique With Gradual Weighting Based on Weather Situation. Gensler, A.; Sick, B. (2016). 4976–4984.
     
  • Design and optimization of an autonomous feature selection pipeline for high dimensional, heterogeneous feature spaces. Schlegel, B.; Sick, B. (2016). 1–9.
     
  • Deep Learning for Solar Power Forecasting -- An Approach using Autoencoder and LSTM Neural Networks. Gensler, A.; Henze, J.; Sick, B.; Raabe, N. (2016). 2858–2865.
     
  • Correlation of Ontology-Based Semantic Similarity and Crowdsourced Human Judgement for a Domain Specific Fashion Ontology. Kalkowski, E.; Sick, B. in Web Engineering, A. Bozzon, P. Cudre-Maroux, C. Pautasso (reds.) (2016). (Vol. 9671) 207–224.
     
  • Coping with variability in motion based activity recognition. Kreil, M.; Sick, B.; Lukowicz, P. (2016). 1–8.
     
  • Combinations of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM. Breker, S.; Sick, B. (2016). 2164–2173.
     
  • Any Problems? A wearable sensor-based platform for representational learning-analytics. Pirkl, G.; Hevesi, P.; Lukowicz, P.; Klein, P.; Heisel, C.; Gröber, S.; Kuhn, J.; Sick, B. (2016). 353–356.
     
  • An Analogue-Based Similarity Search Technique for Solar Power Forecasting. Gensler, A.; Sick, B.; Pankraz, V. (2016). 2850–2857.
     
  • A Review of Deterministic Error Scores and Normalization Techniques for Power Forecasting Algorithms. Gensler, A.; Sick, B.; Vogt, S. (2016). 1–9.
     
  • ``Know thyselves’’ -- Computational Self-Reflection in Collective Technical Systems. Hähner, J.; von Mammen, S.; Timpf, S.; Tomforde, S.; Sick, B.; Geihs, K.; Goeble, T.; Hornung, G.; Stumme, G. (2016). 1–8.
     

2015 [ nach oben ]

  • Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data. Kalkowski, E.; Sick, B. (2015). 1693–1699.
     
  • Transductive active learning -- A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data. Reitmaier, T.; Calma, A.; Sick, B. in Information Sciences (2015). 293 275–298.
     
  • The Responsibility Weighted Mahalanobis Kernel for Semi-Supervised Training of Support Vector Machines for Classification. Reitmaier, T.; Sick, B. in Information Sciences (2015). 323 179–198.
     
  • Runtime Self-Integration as Key Challenge for Mastering Interwoven Systems Workshops. Hähner, J.; Brinkschulte, U.; Lukowicz, P.; Mostaghim, S.; Sick, B.; Tomforde, S. (2015). 1–8.
     
  • Generative Exponential Smoothing Models for Rate Forecasting with Uncertainty Estimation. Kalkowski, E.; Sick, B. (2015). 806–817.
     
  • Fast Feature Extraction for Time Series Analysis Using Least-squares Approximations with Orthogonal Basis Functions. Gensler, A.; Gruber, T.; Sick, B. (2015). 29–37.
     
  • Effiziente Bewertung des Anschlusspotentials von Niederspannungsnetzen für dezentrale Erzeugungsanlagen: Klassifikation mit Methoden der Computational Intelligence. Breker, S.; Sick, B. (2015). 51–56.
     
  • Car Drive Classification and Context Recognition for Personalized Entertainment Preference Learning. Stone, T. C.; Haas, S.; Breitenstein, S.; Wiesner, K.; Sick, B. in International Journal on Advances in Software (2015). 8(1 & 2) 53–64.
     
  • Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations. Breker, S.; Claudi, A.; Sick, B. in IEEE Transactions on Power Systems (2015). 30(2) 689–700.
     
  • Camera Based Pedestrian Path Prediction by Means of Polynominal Least-squares Approximation and Multilayer Perceptron Neural Networks. Goldhammer, M.; Köhler, S.; Doll, K.; Sick, B. (2015). 390–399.
     
  • Bewertung verschiedener Spannungsregelungskonzepte in einem einspeisegeprägten Mittelspannungsnetz und Ausblick auf neue Konzepte basierend auf Methoden der Computational Intelligence. Rudolph, J.; Breker, S.; Sick, B. (2015). 57–63.
     
  • Analyse des Fahrerverhaltens zur Entwicklung von intelligenten Komfortfunktionen. Stone, T. C.; Huber, A.; Siwy, R.; Sick, B. in Elektronik automotive (2015). 2(02) 32–36.
     
  • An Online Influence Detection Algorithm for Organic Computing Systems. Rudolph, S.; Tomforde, S.; Sick, B.; Heck, H.; Wacker, A.; Hähner, J. (2015). 1–8.
     
  • A Tool Chain for Context Detection Automating the Investigation of a Multitude of Parameter Sets. Jahn, A.; Lau, S. L.; David, K.; Sick, B. (2015). 1–5.
     
  • A New Vision of Collaborative Active Learning. Calma, Adrian; Reitmaier, Tobias; Sick, Bernhard; Lukowicz, Paul; Embrechts, Mark in arXiv e-prints (2015). arXiv:1504.00284.
     
  • A Mutual Influence Detection Algorithm for Systems with Local Performance Measurement. Rudolph, S.; Tomforde, S.; Sick, B.; Hähner, J. (2015). 144–149.
     
  • A Generalized Hebb (GH) rule based on a cross-entropy error function for deep belief recursive learning. Embrechts, M.; Sick, B. (2015). 21–24.
     
  • A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection. Gruhl, C.; Sick, B.; Wacker, A.; Tomforde, S.; Hähner, J. (2015). 194–200.
     

2014 [ nach oben ]

  • Temporal data analytics based on eigenmotif and shape space representations of time series. Gensler, A.; Sick, B.; Willkomm, J. (2014). 753–757.
     
  • SMART-iBrush -- Individuelle Unterstützung der Zahnreinigung durch Messung von Bewegung und Druck mit einer intelligenten Zahnbürste. Al-Falouji, G.; Prestel, D.; Scharfenberg, G.; Mandl, R.; Deinzer, A.; Halang, W.; Margraf-Stiksrud, J.; Sick, B.; Deinzer, R. R. Weidner, T. Redlich (reds.) (2014). 315–327.
     
  • Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques. Jänicke, M.; Sick, B.; Lukowicz, P.; Bannach, D. (2014). 128–136.
     
  • Programmierkompetenz prüfen … am Beispiel der Vorlesung "`Einführung in C"’ an der Universität Kassel. Herwig, B.; Frommann, U.; Gruber, T.; Sick, B. in Neues Handbuch Hochschullehre. Lehren und Lernen effizient gestalten, B. Berendt, A. Fleischmann, N. Schaper, B. Szczyrba, J. Wildt (reds.) (2014). 71–94.
     
  • Pedestrian’s Trajectory Forecast in Public Traffic with Artificial Neural Networks. Goldhammer, M.; Doll, K.; Brunsmann, U.; Gensler, A.; Sick, B. (2014). 4110–4115.
     
  • On General Purpose Time Series Similarity Measures and Their Use as Kernel Functions in Support Vector Machines. Pree, H.; Herwig, B.; Gruber, T.; Sick, B.; David, K.; Lukowicz, P. in Information Sciences (2014). 281(10) 478–495.
     
  • Novel Criteria to Measure Performance of Time Series Segmentation Techniques. Gensler, A.; Sick, B.; Pankraz, V. (2014). 192–204.
     
  • Location based learning of user behavior for proactive recommender systems in car comfort functions. Stone, T.; Birth, O.; Gensler, A.; Huber, A.; Jänicke, M.; Sick, B. in Informatik 2014 -- Big Data -- Komplexität meistern, E. Plödereder, L. Grunske, E. Schneider, D. Ull (reds.) (2014). 2121–2132.
     
  • Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications. Fisch, D.; Kalkowski, E.; Sick, B. in IEEE Transactions on Knowledge and Data Engineering (2014). 26(3) 652–666.
     
  • Interwoven Systems. Tomforde, S.; Hähner, J.; Sick, B. in Informatik-Spektrum (2014). 37(5) 483–487.
     
  • Engineering and Mastering Interwoven Systems. Tomforde, S.; Hähner, J.; Seebach, H.; Reif, W.; Sick, B.; Wacker, A.; Scholtes, I. (2014). 1–8.
     
  • Dealing with human variability in motion based, wearable activity recognition. Kreil, M.; Sick, B.; Lukowicz, P. (2014). 36–40.
     
  • Analysis on termination of pedestrians’ gait at urban intersections. Goldhammer, M.; Hubert, A.; Köhler, S.; Zindler, K.; Brunsmann, U.; Doll, K.; Sick, B. (2014). 1758–1763.
     
  • ``Know thyself’’ -- Computational Self-Reflection in Intelligent Technical Systems. Tomforde, S.; Hähner, J.; von Mammen, S.; Gruhl, C.; Sick, B.; Geihs, K. (2014).
     

2013 [ nach oben ]

  • Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS. Reitmaier, T.; Sick, B. in Information Sciences (2013). 230 106–131.
     
  • Classification of Electromyographic Signals: Comparing Evolvable Hardware to Conventional Classifiers. Kaufmann, P.; Glette, K.; Gruber, T.; Platzner, M.; Torresen, J.; Sick, B. in IEEE Transactions on Evolutionary Computation (2013). 17(1) 46–63.
     
  • Blazing Fast Time Series Segmentation Based on Update Techniques for Polynomial Approximations. Gensler, A.; Gruber, T.; Sick, B. (2013). 1002–1011.
     
  • A Concept for Securing Cyber-Physical Systems with Organic Computing Techniques. Hähner, J.; Rudolph, S.; Tomforde, S.; Fisch, D.; Sick, B.; Kopal, N.; Wacker, A. M. Berekovic, M. Danek (reds.) (2013). 1–13.
     

2012 [ nach oben ]

  • Techniques for knowledge acquisition in dynamically changing environments. Fisch, D.; Jänicke, M.; Kalkowski, E.; Sick, B. in ACM Transactions on Autonomous and Adaptive Systems (2012). 7(1) 16:1–16:25.
     
  • Learning from others: Exchange of classification rules in intelligent distributed systems. Fisch, D.; Jänicke, M.; Kalkowski, E.; Sick, B. in Artificial Intelligence (2012). 187--188 90–114.
     
  • Handedness Tests for Preschool Children: A Novel Approach Based on Graphics Tablets and Support Vector Machines. Gruber, T.; Meixner, B.; Prosser, J.; Sick, B. in Applied Soft Computing (2012). 12(4) 1390–1398.
     
  • Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee. Embrechts, M. J.; Gatti, C. J.; Linton, J. D.; Gruber, T.; Sick, B. (2012). 1–8.
     
  • Determination of Optimal CT Scan Parameters Using Radial Basis Function Neural Networks. Giedl-Wagner, R.; Miller, T.; Sick, B. (2012). 221–228.
     

2011 [ nach oben ]

  • Wissenschaftspropädeutisches Arbeiten im W-Seminar: Grundlagen -- Chancen -- Herausforderungen. Gottfried, T.; Fliege, R.; Frömberg, J.; Heckmann, G.; Sick, B.; Triller, U.; Wunsch, M. (2011).
     
  • SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis. Fisch, D.; Gruber, T.; Sick, B. in IEEE Transactions on Knowledge and Data Engineering (2011). 23(5) 774–787.
     
  • On-Line Intrusion Alert Aggregation With Generative Data Stream Modeling. Hofmann, A.; Sick, B. in IEEE Transactions on Dependable and Secure Computing (2011). 8(2) 282–294.
     
  • Learning. Sick, B. in Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.) (2011). 235–236.
     
  • In your interest: Objective interestingness measures for a generative classifier. Fisch, D.; Kalkowski, E.; Sick, B.; Ovaska, S. (2011). 414–423.
     
  • Divergence Measures as a Generalised Approach to Quantitative Emergence. Fisch, D.; Jänicke, M.; Müller-Schloer, C.; Sick, B. in Organic Computing --- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.) (2011). 53–66.
     
  • Collaborative Learning by Knowledge Exchange. Fisch, D.; Kalkowski, E.; Sick, B. in Organic Computing -- A Paradigm Shift for Complex Systems, C. Müller-Schloer, H. Schmeck, T. Ungerer (reds.) (2011). 267–280.
     
  • Automatic Adaptation of Mobile Activity Recognition Systems to New Sensors. Bannach, D.; Sick, B.; Lukowicz, P. (2011). 1–5.
     
  • Active classifier training with the 3DS strategy. Reitmaier, T.; Sick, B. (2011). 88–95.
     

2010 [ nach oben ]

  • Temporal Data Mining Using Shape Space Representations of Time Series. Fuchs, E.; Gruber, T.; Pree, H.; Sick, B. in Neurocomputing (2010). 74(1--3) 379–393.
     
  • Quantitative Emergence -- A Refined Approach Based on Divergence Measures. Fisch, D.; Jänicke, M.; Sick, B.; Müller-Schloer, C. (2010). 94–103.
     
  • Online Signature Verification With Support Vector Machines Based on LCSS Kernel Functions. Gruber, C.; Gruber, T.; Krinninger, S.; Sick, B. in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) (2010). 40(4) 1088–1100.
     
  • Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations. Fuchs, E.; Gruber, T.; Nitschke, J.; Sick, B. in IEEE Transactions on Pattern Analysis and Machine Intelligence (2010). 32(12) 2232–2245.
     

2007 [ nach oben ]

  • Towards an Automated Analysis of Neuroleptics’ Impact on Human Hand Motor Skills. Dose, Matthias; Gruber, Christian; Grunz, Ariane; Hook, Christian; Kempf, Jürgen; Scharfenberg, Georg; Sick, Bernhard (2007). 494–501.
     

2006 [ nach oben ]

  • Biometric Analysis of Handwriting Dynamics Using a Script Generator Model. Hofer, J.; Gruber, C.; Sick, B. (2006). 36–41.