  | Aftiss, A., LAMSIYAH, S., SCHOMMER, C., & El Alaoui Ouatik, S. (2024). Abstractive Biomedical Long Document Summarization Through Zero-Shot Prompting. In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS) (pp. 1-6). Marrakech, Morocco: IEEE. doi:10.1109/icds62089.2024.10756301  Peer reviewed |
  | HOSSEINI KIVANANI, N., Salobrar-García, E., Elvira-Hurtado, L., Salas, M., SCHOMMER, C., & LEIVA, L. A. (2024). Predicting Alzheimer’s Disease and Mild Cognitive Impairment with Off-line and On-line House Drawing Tests. In 2024 IEEE 20th International Conference on e-Science (e-Science) (pp. 1--10). IEEE. doi:10.1109/e-science62913.2024.10678661 Peer reviewed |
LAMSIYAH, S., El Mahdaouy, A., NOURBAKHSH, A., & SCHOMMER, C. (2024). Fine-Tuning a Large Language Model with Reinforcement Learning for Educational Question Generation. In Lecture Notes in Computer Science. recife, Brazil: Springer Nature Switzerland. doi:10.1007/978-3-031-64302-6_30 Peer reviewed |
 | SCHOMMER, C. (19 January 2024). Auf dem Weg zum autonomen Fahren. D'Lëtzebuerger Land, 19 January 2024, p. 2. |
 | Aftiss, A., LAMSIYAH, S., Ouatik El Alaoui, S., & SCHOMMER, C. (2024). BioMDSum: An Effective Hybrid Biomedical Multi-Document Summarization Method Based on PageRank and Longformer Encoder-Decoder. IEEE Access, 12, 188013 - 188031. doi:10.1109/ACCESS.2024.3514915 Peer Reviewed verified by ORBi |
 | HOSSEINI KIVANANI, N.* , SCHOMMER, C., & LEIVA, L. A. (2023). The Magic Number: Impact of Sample Size for Dementia Screening Using Transfer Learning and Data Augmentation of Clock Drawing Test Images. In The Magic Number: Impact of Sample Size for Dementia Screening Using Transfer Learning and Data Augmentation of Clock Drawing Test Images. China: IEEE. doi:10.1109/healthcom56612.2023.10472399 Peer reviewed |
 | Mahdaouy, A., LAMSIYAH, S., Alami, H., SCHOMMER, C., & Berrada, I. (2023). UM6P & UL at WojoodNER shared task: Improving Multi-Task Learning for Flat and Nested Arabic Named Entity Recognition. In UM6P & UL at WojoodNER shared task: Improving Multi-Task Learning for Flat and Nested Arabic Named Entity Recognition. Singapore, Singapore: Association for Computational Linguistics (ACL). Peer reviewed |
 | LAMSIYAH, S., Mahdaouy, A., Alami, H., Berrada, I., & SCHOMMER, C. (2023). UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic. In UL & UM6P at ArAIEval Shared Task: Transformer-based model for Persuasion Techniques and Disinformation detection in Arabic (pp. 777–782). Singapore, Singapore: Association for Computational Linguistics (ACL). Peer reviewed |
 | SCHOMMER, C., LAMSIYAH, S., & NOUZRI, S. (2023). Über den Einsatz von Generative.AI für die Lehre. Luxemburger Wort. |
 | HOSSEINI KIVANANI, N., Salobrar-García, E., Elvira-Hurtado, L., López-Cuenca, I., De Hoz, R., Ramírez, J. M., Gil, P., Salas, M., SCHOMMER, C., & LEIVA, L. A. (2023). Better Together: Combining Different Handwriting Input Sources Improves Dementia Screening. In Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023 (pp. 1-7). Institute of Electrical and Electronics Engineers Inc. doi:10.1109/e-Science58273.2023.10254799 Peer reviewed |
 | LAMSIYAH, S., MURUGARAJ, K., & SCHOMMER, C. (2023). Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization. In Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization. London, United Kingdom: https://avestia.com/. doi:10.11159/cist23.152 Peer reviewed |
 | HOSSEINI KIVANANI, N., Vásquez-Correa, J. C., SCHOMMER, C., & Nöth, E. (2023). EXPLORING THE USE OF PHONOLOGICAL FEATURES FOR PARKINSON’S DISEASE DETECTION. In N. HOSSEINI KIVANANI, J. C. Vásquez-Correa, C. SCHOMMER, ... E. Nöth, EXPLORING THE USE OF PHONOLOGICAL FEATURES FOR PARKINSON’S DISEASE DETECTION (pp. 3897-3901). Peer reviewed |
 | LAMSIYAH, S., El Mahdaouy, A., & SCHOMMER, C. (2023). Can Anaphora Resolution Improve Extractive Query-Focused Multi-Document Summarization? IEEE Access, 1-1. doi:10.1109/ACCESS.2023.3314524 Peer Reviewed verified by ORBi |
 | LAMSIYAH, S., & SCHOMMER, C. (2023). A Comparative Study of Sentence Embeddings for Unsupervised Extractive Multi-document Summarization. In Artificial Intelligence and Machine Learning (pp. 78--95). Cham, Unknown/unspecified: Springer Nature Switzerland. doi:10.1007/978-3-031-39144-6_6 Peer reviewed |
 | LAMSIYAH, S., El Mahdaouy, A., Alami, H., Berrada, I., & SCHOMMER, C. (2023). UL \& UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) (pp. 644--650). Toronto, Canada, Unknown/unspecified: Association for Computational Linguistics. doi:10.18653/v1/2023.semeval-1.88 Peer reviewed |
 | NAJJAR, A., HOSSEINI KIVANANI, N., TCHAPPI HAMAN, I., Mualla, Y., Van der Peijl, E., KARPATI, D., & SCHOMMER, C. (2022). XAI: Using Smart Photobooth for Explaining History of Art. In XAI: Using Smart Photobooth for Explaining History of Art (pp. 256-259). doi:10.1145/3527188.3563914 Peer reviewed |
HOSSEINI KIVANANI, N., Asadi, H., SCHOMMER, C., & Volker, D. (July 2022). A comparative study of automatic classifiers to recognize speakers based on fricatives [Poster presentation]. 1st Interdisciplinary Conference on Voice Identity (VoiceID): Perception, Production, and Computational Approaches, Zurich, Switzerland. |
 | SIRAJZADE, J., BOUVRY, P., & SCHOMMER, C. (2022). Deep Mining Covid-19 Literature. In Applied Informatics, 5th International Conference, ICAI 2022, Arequipa, Peru, October 27–29, 2022, Proceedings (pp. 121–133). Springer Cham. doi:10.1007/978-3-031-19647-8_9 Peer reviewed |
 | LEIVA, L. A., PRUSKI, C., MARKOVICH, R., NAJJAR, A., & SCHOMMER, C. (Eds.). (2022). Artificial Intelligence and Machine Learning - 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, November 10-12, 2021, Revised Selected Papers. Springer. |
 | SCHOMMER, C. (2021). About AI and Arts [Paper presentation]. AI and Arts Workshop, Belval-Université, Luxembourg. |
 | SCHOMMER, C. (2021). Future living with AI and IA [Paper presentation]. 33rd Benelux Conference on Artificial Intelligence (BNAIC), Belval-Université, Luxembourg. |
 | SCHOMMER, C. (2021). The Future of Living with AI [Paper presentation]. International Symposium "The Future of Living"; https://www.bozar.be/en/calendar/symposium-future-living, Brussels, Belgium. |
 | SCHOMMER, C. (2021). Getting Creative - AI and Arts [Paper presentation]. AIFA - Artificial Intelligence and the Future of Arts 2021, CCH, UL, Luxembourg. |
 | SCHOMMER, C. (2021). Ist die Künstliche Intelligenz für oder gegen die Menschheit? [Paper presentation]. Les cylcles de l'UNESCO, Bibliothèque Nationale du Luxembourg, Kirchberg, Luxembourg. |
 | LEIVA, L. A., PRUSKI, C., MARKOVICH, R., NAJJAR, A., & SCHOMMER, C. (Eds.). (2021). Proceedings of BNAIC/BeneLearn 2021. Luxembourg: BnL. |
 | SCHOMMER, C., SAUTER, T., PANG, J., SATAGOPAM, V., DESPOTOVIC, V., & GONCALVES, J. (2021). Proceedings of the AI4Health Lecture Series (2021) [Paper presentation]. AI4Health Lectures Series (2021), Campus Belval, University of Luxembourg, Luxembourg. |
 | SCHOMMER, C. (2020). The potential of Language Technology and AI [Paper presentation]. European Language Resource Coordination (ELRC) Workshop. |
 | Sharma, R., SCHOMMER, C., & Vivarelli, N. (2020). Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling. In R. Sharma, Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling (pp. 2). Peer reviewed |
 | KAMLOVSKAYA, E., & SCHOMMER, C. (29 September 2020). Using word embeddings to explore the Aboriginality discourse in a corpus of Australian Aboriginal autobiographies [Paper presentation]. Synergies: Bridging the Gap Between Traditional and Digital Literary Studies, Denmark. |
 | SIRAJZADE, J., GIERSCHEK, D., & SCHOMMER, C. (2020). An Annotation Framework for Luxembourgish Sentiment Analysis. In L. Besacier, S. Sakti, C. Soria, ... D. Beermann (Eds.), Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) (pp. 172-176). Paris, France: European Language Resources Association (ELRA). Peer reviewed |
 | SIRAJZADE, J., GIERSCHEK, D., & SCHOMMER, C. (2020). Component Analysis of Adjectives in Luxembourgish for Detecting Sentiments. In D. Beermann, L. Besacier, S. Sakti, ... C. Soria (Eds.), Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop(SLTU-CCURL 2020) (pp. 159-166). Paris, France: European Language Resources Association (ELRA). Peer reviewed |
 | SCHOMMER, C. (2020). Eine Doktorarbeit zu beginnen, ist (relativ) leicht. Luxemburger Wort, p. 10. |
 | SCHOMMER, C. (2020). Zwei Doktorarbeiten zwischen Geist und Informatik. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/48719. |
 | SCHOMMER, C., SAUTER, T., PANG, J., & ABANKWA, D. (2020). Proceedings of the AI4Health Lecture Series (2020) [Paper presentation]. AI4Health Lectures Series (2020), Campus Belval, University of Luxembourg, Luxembourg. |
 | DESPOTOVIC, V., Skovranek, T., & SCHOMMER, C. (2020). Speech Based Estimation of Parkinson’s Disease Using Gaussian Processes and Automatic Relevance Determination. Neurocomputing, 401, 173-181. doi:10.1016/j.neucom.2020.03.058 Peer Reviewed verified by ORBi |
 | GUO, S., Höhn, S., & SCHOMMER, C. (2019). A Personalized Sentiment Model with Textual and Contextual Information. In The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019. Peer reviewed |
 | SCHOMMER, C. (19 October 2019). Künstliche Intelligenz für die Medizin. Luxemburger Wort, 244 (171), p. 16-17. |
 | GUO, S., Höhn, S., & SCHOMMER, C. (2019). Looking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model. In ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019. Peer reviewed |
 | GUO, S., Höhn, S., & SCHOMMER, C. (2019). Topic-based Historical Information Selection for Personalized Sentiment Analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019. Peer reviewed |
 | SCHOMMER, C. (03 January 2019). Ein europäisches CERN für die Künstliche Intelligenz. Luxemburger Wort, 170 (298), p. 18. |
 | GUO, S., Höhn, S., Xu, F., & SCHOMMER, C. (2019). Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model. In Agents and Artificial Intelligence. Springer. doi:10.1007/978-3-030-05453-3_10 Peer reviewed |
 | SIRAJZADE, J., & SCHOMMER, C. (2019). The LuNa Open Toolbox for the Luxembourgish Language. In P. Perner (Ed.), Advances in Data Mining, Applications and Theoretical Aspects, Poster Proceedings 2019. Leipzig, Germany: ibai publishing. |
 | GIERSCHEK, D., GILLES, P., PURSCHKE, C., SCHOMMER, C., & SIRAJZADE, J. (2019). A Temporal Warehouse for Modern Luxembourgish Text Collections [Paper presentation]. DHBeNeLux 2019, Liège, Belgium. |
 | Zheng, Y., GUO, S., & SCHOMMER, C. (2018). An Approach to Incorporate Emotions in a Chatbot with Seq2Seq Model. In Benelux Conference on Artificial Intelligence, ‘s-Hertogenbosch 8-9 November 2018. Peer reviewed |
 | GUO, S., & SCHOMMER, C. (10 September 2018). A Bilingual Study for Personalized Sentiment Model PERSEUS [Paper presentation]. PhD Forum at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Dublin, Ireland. |
 | Leyers, P., & SCHOMMER, C. (Other coll.). (2018). Maschinen nach menschlichem Vorbild. Luxemburger Wort. |
 | KAMLOVSKAYA, E., SCHOMMER, C., & SIRAJZADE, J. (2018). A Dynamic Associative Memory for Distant Reading. In International Conference on Artificial Intelligence Humanities, Book of Abstracts. Seoul, South Korea: Chung-Ang University. |
 | SIRAJZADE, J., & SCHOMMER, C. (2018). Mind and Language. AI in an Example of Similar Patterns of Luxembourgish Language. In International Conference on Artificial Intelligence Humanities, Book of Abstracts (pp. 2). Seoul, South Korea: Chung-Ang University. |
 | Bustan S, Gonzalez-Roldan AM, SCHOMMER, C., Kamping S, Löffler M, Brunner M, Flor H, & ANTON, F. (2018). Psychological, cognitive factors and contextual influences in pain and pain-related suffering as revealed by a combined qualitative and quantitative assessment approach. PLoS ONE. doi:10.1371/journal.pone.0199814 Peer Reviewed verified by ORBi |
 | BOUVRY, P., Bisdorff, R., SCHOMMER, C., SORGER, U., THEOBALD, M., & VAN DER TORRE, L. (2018). Proceedings - 2017 ILILAS Distinguished Lectures. Luxembourg, Luxembourg: University of Luxembourg. https://orbilu.uni.lu/handle/10993/33848 |
 | VIJAYAKUMAR, B., Höhn, S., & SCHOMMER, C. (2018). Quizbot: Exploring Formative Feedback with Conversational Interfaces. In B. VIJAYAKUMAR, S. Höhn, ... C. SCHOMMER, Proceedings of the. Springer. Peer reviewed |
 | GUO, S., Höhn, S., Xu, F., & SCHOMMER, C. (2018). PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network. In International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018 (pp. 9). Peer reviewed |
 | GUO, S., & SCHOMMER, C. (2017). Embedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion. In International Conference on Companion Technology, Ulm 11-13 September 2017. IEEE. Peer reviewed |
 | van Dijk, T., & SCHOMMER, C. (Eds.). (2017). Proceedings of the 2nd International Workshop on Exploring Old Maps. (2nd). Würzburg, Germany: University of Würzburg. |
 | SCHOMMER, C. (2017). Q&A with Data Scientists: Christopher Schommer. Operational Database Management Systems. Peer reviewed |
 | Höhn, W., & SCHOMMER, C. (2017). Georeferencing of Place Markers in Digitized Early Maps by Using Similar Maps as Data Source. In Digital Humanities 2017: Conference Abstracts. Peer reviewed |
 | Höhn, W., & SCHOMMER, C. (2017). RAT 2.0. In Digital Humanities 2017: Conference Abstracts. Peer reviewed |
| Bustan, S., Gonzalez-Roldan, A. M., SCHOMMER, C., Kamping, S., Loeffler, M., Brunner, M., Flor, H., & ANTON, F. (November 2016). Facteurs psychologiques, cognitifs et les influences contextuelles dans la douleur et la souffrance liée à la douleur [Poster presentation]. 16e congrès national de la société française d'étude et de traitement de la douleur (SFETD), Bordeaux, France. |
Höhn, W., & SCHOMMER, C. (July 2016). Annotating and Georeferencing of Digitized Early Maps [Poster presentation]. Digital Humanities 2016. |
 | van Dijk, T., & SCHOMMER, C. (Eds.). (2016). Proceedings International Workshop Exploring Old Maps 2016. Luxembourg, Luxembourg: University of Luxembourg. |
 | Höhn, W., & SCHOMMER, C. (June 2016). RAT: A Referencing and Annotation Tool for Digitized Early Maps [Paper presentation]. DHBenelux Cconference 2016, Luxembourg. |
 | Höhn, S., Busemann, S., MAX, C., SCHOMMER, C., & ZIEGLER, G. (September 2015). Interaction Profiles for an Artificial Conversational Companion [Paper presentation]. International Symposium on Companion Technology, Ulm, Germany. |
 | SCHOMMER, C. (2014). Sentiment Barometer in Financial News. (-). Luxembourg, Luxembourg: Internal Report. https://orbilu.uni.lu/handle/10993/20930 |
 | Bouleau, F., & SCHOMMER, C. (2014). Finding Outliers in Satellite Patterns by Learning Pattern Identities. In J. Filipe & A. Fred (Eds.), Proceedings "6th International Conference on Agents an Artificial Intelligence". doi:10.5220/0004814301130120 Peer reviewed |
 | KAMPAS, D., SCHOMMER, C., & SORGER, U. (2014). A Hidden Markov Model to detect relevance in nancial documents based on on/off topics. European Conference on Data Analysis. Peer reviewed |
 | MINEV, M., & SCHOMMER, C. (2013). News Representation with Multi-Word Features. In Proceedings ECDA. Peer reviewed |
 | SCHOMMER, C., KAMPAS, D., & BERSAN, R. (2013). A Prospect on How to Find the Polarity of a Financial News by Keeping an Objective Standpoint. In Proceedings ICAART 2013. Peer reviewed |
 | Bouleau, F., & SCHOMMER, C. (2013). Outlier Identification in Spacecraft Monitoring Data using Curve Fitting Information. Proceedings ECDA. Peer reviewed |
 | DANILAVA, S., Busemann, S., SCHOMMER, C., & ZIEGLER, G. (2013). Towards Computational Models for a Long-term Interaction with an Artificial Conversational Companion. In Proceedings "5th International Conference on Agents an Artificial Intelligence" (pp. 241-248). Peer reviewed |
 | KAMPAS, D., & SCHOMMER, C. (2013). A Hybrid Classification System to find News that is relevant. ECDA 2013. Peer reviewed |
 | DANILAVA, S., Busemann, S., SCHOMMER, C., & ZIEGLER, G. (2013). Why are you Silent? - Towards Responsiveness in Chatbots. Avec le Temps! Time, Tempo, and Turns in Human-Computer Interaction". Workshop at CHI 2013, Paris, France. Peer reviewed |
 | SCHOMMER, C., MINEV, M., GRAMMATIKOS, T., & Schaefer, U. (2013). Feature Extraction and Representation for Economic Surveys [Poster presentation]. Bridging between Information Retrieval and Databases. |
 | MINEV, M., & SCHOMMER, C. (2013). Domain-driven news representation using conditional attribute-value pairs. In N. Ferro (Ed.), PROMISE Winter School 2013: Bridging between Information Retrieval and Databases. Springer Publishing. Peer reviewed |
 | MINEV, M., SCHOMMER, C., & GRAMMATIKOS, T. (2012). News and stock markets: A survey on abnormal returns and prediction models. Technical Report, UL. https://orbilu.uni.lu/handle/10993/14176 |
 | PORAY, J., & SCHOMMER, C. (2012). Operations on Conversational Mind-Graphs. In Proceedings "4th International Conference on Agents and Artificial Intelligence" (pp. 511-514). SciTePress. Peer reviewed |
DANILAVA, S., SCHOMMER, C., & ZIEGLER, G. (2012). Long-term Human-machine Interaction: Organisation and Adaptability of Talk-in-interaction. CHIST-ERA Conference, Edinburgh, Scotland. Peer reviewed |
 | DANILAVA, S., Busemann, S., & SCHOMMER, C. (2012). Artificial Conversational Companions A Requirements Analysis. In Proceedings "4th International Conference on Agents and Artificial Intelligence" (pp. 282-289). SciTePress 2012. doi:10.5220/0003834702820289 Peer reviewed |
 | PORAY, J., & SCHOMMER, C. (2010). Managing Conversational Streams with Explorative Mind-Maps. In Proceedings "AICCSA" (pp. 1 - 4). doi:10.1109/AICCSA.2010.5587033 Peer reviewed |
 | KAUFMANN, S., & SCHOMMER, C. (2010). Towards E-Conviviality in Web-Based Systems by considering the Wisdom of Crowds. Abstract book of 2nd Conference on Agents and Artificial Intelligence (ICAART 2010), 305 - 307. Peer reviewed |
 | SCHOMMER, C. (2010). A Molecular Concept of Managing Data. Abstract book of 2nd Conference on Agents and Artificial Intelligence (ICAART 2010), 300-305. Peer reviewed |
 | PORAY, J., & SCHOMMER, C. (2009). A Cognitive Mind-Map Framework to Foster Trust. In Proceedings "Fifth International Conference on Natural Computation, ICNC 2009" (pp. 3-7). doi:10.1109/ICNC.2009.614 Peer reviewed |
 | KAUFMANN, S., & SCHOMMER, C. (2009). e-Conviviality in Web Systems by the Wisdom of Crowds. Abstract book of 4th ICITST09 - International Conference for Internet Technology and Secured Transactions, 74-76. doi:10.1109/icitst.2009.5402530 Peer reviewed |
SCHOMMER, C. (2008). Sieving publishing communities in DBLP. In Proceedings "Third IEEE International Conference on Digital Information Management (ICDIM)" (pp. 621-625). doi:10.1109/ICDIM.2008.4746753 Peer reviewed |
KAUFMANN, S., SCHOMMER, C., & Weires, R. (2008). SEREBIF - Search Engine Result Enhancement by Implicit Feedback. In Proceedings WebIst (pp. 263-266). Peer reviewed |
BRUCKS, C., HILKER, M., SCHOMMER, C., WAGNER, C., & Weires, R. (2008). Symbolic Computing with Incremental Mind-maps to Manage and Mine Data Streams - Some Applications. Abstract book of 4th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'08). ECAI 2008, 120-125. Peer reviewed |
HILKER, M., & SCHOMMER, C. (2008). Service Oriented Architecture in Network Security - a novel Organisation in Security Systems. Abstract book of 3rd International Workshop on Theory of Computer Viruses (TCV 2008), 120-125. Peer reviewed |
BRUCKS, C., HILKER, M., SCHOMMER, C., WAGNER, C., & Weires, R. (2007). Semi-automated Content Zoning of Spam Emails. Lecture Notes on Business Information Processing. Web Information Systems and Technologies, 35-44. Peer reviewed |
BRUCKS, C., HILKER, M., SCHOMMER, C., WAGNER, C., & Weires, R. (2007). Semi-automated Content Zoning of Spam Emails. In Proceedings "WebIst" (pp. 35-44). Peer reviewed |
HILKER, M., & SCHOMMER, C. (2007). SANA - Network Protection through artificial Immunity. Abstract book of 2nd International Workshop on Theory of Computer Viruses (TCV 2007), 120-125. Peer reviewed |
HILKER, M., & SCHOMMER, C. (2006). AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies. |
HILKER, M., & SCHOMMER, C. (2006). SANA - Security Analysis in Internet Traffic through Artificial Immune Systems. Technical Documentary Report. United States. Air Force. Systems Command. Electronic Systems Division. Peer reviewed |
HILKER, M., & SCHOMMER, C. (2006). Description of bad-signatures for Network Intrusion Detection. Proceedings of the 4th. Australasian Information Security Workshop (AISW-NetSec 2006). Peer reviewed |
HILKER, M., & SCHOMMER, C. (2006). AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies. Abstract book of 6th International Conference on Recent Advances in Soft Computing (RASC 2006), 120-125. Peer reviewed |
HILKER, M., & SCHOMMER, C. (2005). A new queueing strategy for the Adversarial Queueing Theory. IPSI-2005, 120-125. Peer reviewed |
SCHOMMER, C., & SCHROEDER, B. (2005). ANIMA: Associate Memories for Categorical data Streams. Abstract book of 3rd International Conference on Computer Science and its Applications (ICCSA-2005), 120-125. Peer reviewed |
Sun, Q., SCHOMMER, C., & Lang, A. (2004). Integration of Manual and Automatic Text Categorization. A Categorization Workbench for Text-Based Email and Spam. In Proceedings KI 2004 (pp. 156-167). Peer reviewed |
SCHROEDER, B., Hansen, F., & SCHOMMER, C. (2004). A methodology for pattern discovery in tennis rallys using the adaptive framework ANIMA. Workshop Knowledge Discovery in Data Streams. ECML/PKDD 2005, 10-15. Peer reviewed |
SCHOMMER, C. (2004). Incremental Discovery of Association Rules with Dynamic Neural Cells. Workshop on Symbolic Networks. ECAI 2004, 10-15. Peer reviewed |
SCHOMMER, C. (2004). An SQL-like interface to retrieve associative patterns from neural skeletons. Proceedings 2004 International Conference on Advances in Intelligent Systems - Theory and Applications, 80-85. Peer reviewed |
SCHOMMER, C. (2004). An incremental neural-based method to discover temporal skeletons in transactional data streams. Abstract book of 5th Recent Advances in Soft Computing (RASC 2004), 80-85. Peer reviewed |
SCHOMMER, C. (2003). Anwendung von Data Mining. Shaker Publishing. |
BAYERL, S., Bollinger, T., & SCHOMMER, C. (2002). Applying Mining with Scoring. In Data Mining III, 6 (pp. 757-766). WIT Press. Peer reviewed |
Müller, U., & SCHOMMER, C. (2001). Data Mining im eCommerce: ein Fallbeispiel zur erweiterten Logfile-Analyse. In HMD - Praxis der Wirtschaftsinformatik, 06/2001 (pp. 59-69). dpunkt Publishing. Peer reviewed |
SCHOMMER, C. (2001). Konfirmative und explorative Synergiewirkungen im erkenntnisorientierten Informationsyzyklus von BAIK. AKA Publishing. |
ANDERSEN, C., BAYERL, S., Bent, G., Lee, J., & SCHOMMER, C. (2001). Mining your own Business in Retail Volume 1 - Retail. United States: IBM Press. |
ANDERSEN, C., BAYERL, S., Bent, G., Lee, J., & SCHOMMER, C. (2001). Mining your own Business Vol. 4 - Health Care. United States: IBM Press. |
ANDERSEN, C., BAYERL, S., Bent, G., Lee, J., & SCHOMMER, C. (2001). Mining your own Business Vol. 3 - Telecommunication. United States: IBM Press. |
ANDERSEN, C., BAYERL, S., Bent, G., Lee, J., & SCHOMMER, C. (2001). Mining your own Business Vol 2 - Finance. United States: IBM Press. |
Kemke, C., & SCHOMMER, C. (1993). PAPADEUS - Parallel Parsing of Ambiguous Sentences. Proceedings of the World Congress on Neural Networks, 79-82. Peer reviewed |
Dataset: