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Books and Theses
- 2024
[b3]Gerhard Paaß:
Artificial Intelligence - What Is Behind the Technology of the Future? Springer 2024, ISBN 978-3-031-50604-8, pp. 1-474- 2023
- [b2]Gerhard Paaß, Sven Giesselbach
:
Foundation Models for Natural Language Processing - Pre-trained Language Models Integrating Media. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2023, ISBN 978-3-031-23189-6, pp. 1-419 - 1984
- [b1]Gerhard Paaß:
Prognose und Asymptotik Bayesscher Modelle: e. Methode zur Evaluation d. Genauigkeit ökonometr. Prognosen. University of Bonn, Germany, Oldenbourg 1984, ISBN 978-3-486-28671-7, pp. 1-310
Journal Articles
- 2023
- [j12]Dirk Hecker, Angi Voss, Gerhard Paaß, Tim Wirtz:
Big Data 2.0 - mit synthetischen Daten KI-Systeme stärken. Wirtschaftsinformatik Manag. 15(2): 161-167 (2023) - 2011
- [j11]Donald E. Brown, Fazel Famili, Gerhard Paass, Kate Smith-Miles, Lyn C. Thomas, Richard Weber, Ricardo Baeza-Yates, Cristián Bravo, Gaston L'Huillier, Sebastián Maldonado:
Future trends in business analytics and optimization. Intell. Data Anal. 15(6): 1001-1017 (2011) - 2010
- [j10]André Bergholz, Jan De Beer, Sebastian Glahn, Marie-Francine Moens, Gerhard Paaß, Siehyun Strobel:
New filtering approaches for phishing email. J. Comput. Secur. 18(1): 7-35 (2010) - 2007
- [j9]Edda Leopold, Jörg Kindermann, Gerhard Paaß:
Analysis of E-Discussions Using Classifier Induced Semantic Spaces. LDV Forum 22(1): 21-27 (2007) - 2005
- [j8]Andreas Hotho, Andreas Nürnberger, Gerhard Paass:
A Brief Survey of Text Mining. LDV Forum 20(1): 19-62 (2005) - 2004
- [j7]René Cavet, Stephan Volmer, Edda Leopold, Jörg Kindermann, Gerhard Paaß:
Revealing the connoted visual code: a new approach to video classification. Comput. Graph. 28(3): 361-369 (2004) - 2003
- [j6]Joachim Diederich, Jörg Kindermann, Edda Leopold, Gerhard Paass:
Authorship Attribution with Support Vector Machines. Appl. Intell. 19(1-2): 109-123 (2003) - [j5]Gerhard Paass, Jörg Kindermann:
Bayesian regression mixtures of experts for geo-referenced data. Intell. Data Anal. 7(6): 567-582 (2003) - 1998
- [j4]Gerhard Paass, Jörg Kindermann:
Klassifikation mit Populationen von heterogenen Modellen. Künstliche Intell. 12(2): 56-65 (1998) - 1994
- [j3]Knut Möller, Gerhard Paass:
Künstliche Neuronale Netze: eine Bestandsaufnahme. Künstliche Intell. 8(4): 37-61 (1994) - 1993
- [j2]Alexander Linden, Gerhard Paass:
Bericht von der Konferenz "Neural Information Processing Szstems - Natural and Synthetic" (NIPS) 1992. Künstliche Intell. 7(3): 26-29 (1993) - 1992
- [j1]Jochen Heinsohn, Gerhard Paass:
European Conference on Symbolic and Quantitative Approaches for Uncertainty. Künstliche Intell. 6(2): 42 (1992)
Conference and Workshop Papers
- 2012
- [c36]Anja Pilz, Gerhard Paaß:
Collective Search for Concept Disambiguation. COLING 2012: 2243-2258 - [c35]Gerhard Paaß, André Bergholz, Anja Pilz:
A knowledge-extraction approach to identify and present verbatim quotes in free text. I-KNOW 2012: 31 - 2011
- [c34]Anja Pilz, Gerhard Paaß:
From names to entities using thematic context distance. CIKM 2011: 857-866 - 2010
- [c33]Frank Reichartz, Hannes Korte, Gerhard Paass:
Semantic relation extraction with kernels over typed dependency trees. KDD 2010: 773-782 - 2009
- [c32]Frank Reichartz, Hannes Korte, Gerhard Paass:
Composite Kernels For Relation Extraction. ACL/IJCNLP (2) 2009: 365-368 - [c31]Tamás Horváth, Gerhard Paass, Frank Reichartz, Stefan Wrobel:
A Logic-Based Approach to Relation Extraction from Texts. ILP 2009: 34-48 - [c30]Anja Pilz, Gerhard Paaß:
Named Entity Resolution Using Automatically Extracted Semantic Information. LWA 2009: KDML:84-91 - [c29]Frank Reichartz, Hannes Korte, Gerhard Paass:
Dependency Tree Kernels for Relation Extraction from Natural Language Text. ECML/PKDD (2) 2009: 270-285 - [c28]Gerhard Paaß, Frank Reichartz:
Exploiting Semantic Constraints for Estimating Supersenses with CRFs. SDM 2009: 485-496 - [c27]Gerhard Paass, Anja Pilz, Jochen Schwenninger:
Named Entity Recognition of Spoken Documents Using Subword Units. ICSC 2009: 529-534 - 2008
- [c26]André Bergholz, Jeong Ho Chang, Gerhard Paass, Frank Reichartz, Siehyun Strobel:
Improved Phishing Detection using Model-Based Features. CEAS 2008 - [c25]André Bergholz, Gerhard Paass, Frank Reichartz, Siehyun Strobel, Marie-Francine Moens, Brian Witten:
Detecting Known and New Salting Tricks in Unwanted Emails. CEAS 2008 - [c24]Christina Lioma, Marie-Francine Moens, Juan-Carlos Gomez, Jan De Beer, André Bergholz, Gerhard Paass, Patrick Horkan:
Anticipating Hidden Text Salting in Emails. RAID 2008: 396-397 - 2005
- [c23]Gerhard Paaß, Hugo de Vries:
Evaluating the Performance of Text Mining Systems on Real-world Press Archives. GfKl 2005: 414-421 - 2004
- [c22]Gerhard Paaß, Jörg Kindermann, Edda Leopold:
Learning Prototype Ontologies by Hierachical Latent Semantic Analysis. LWA 2004: 193-205 - 2002
- [c21]Martha A. Larson, Stefan Eickeler, Gerhard Paaß, Edda Leopold, Jörg Kindermann:
Exploring sub-word features and linear support vector machines for German spoken document classification. INTERSPEECH 2002: 1989-1992 - [c20]Gerhard Paass, Edda Leopold, Martha A. Larson, Jörg Kindermann, Stefan Eickeler:
SVM Classification Using Sequences of Phonemes and Syllables. PKDD 2002: 373-384 - 2001
- [c19]Jörg Kindermann, Gerhard Paass, Edda Leopold:
Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization. PKDD 2001: 266-276 - 2000
- [c18]Byoung-Tak Zhang, Gerhard Paass, Heinz Mühlenbein:
Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains. CEC 2000: 938-945 - [c17]Gerhard Paass, Jörg Kindermann:
Multistep Sequential Exploration of Growing Bayesian Classification Models. IJCNN (3) 2000: 566-574 - [c16]Gerhard Paass, Jörg Kindermann:
Transparency and Predicive Power: Explaining Complex Classification Models. PKDD 2000: 558-565 - 1998
- [c15]Gerhard Paass, Jörg Kindermann:
Bayesian Classification Trees with Overlapping Leaves Applied to Credit-Scoring. PAKDD 1998: 234-245 - [c14]Jörg Kindermann, Gerhard Paass:
Model Switching for Bayesian Classification Trees with Soft Splits. PKDD 1998: 148-157 - 1996
- [c13]Heinz Mühlenbein, Gerhard Paass:
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. PPSN 1996: 178-187 - 1995
- [c12]Joachim Diederich, Don Simonetta, Gerhard Paass:
Estimating Blue-Green Algae Counts with Neurocomputing Techniques. IEA/AIE 1995: 127-131 - 1994
- [c11]Gerhard Paass, Jörg Kindermann:
Bayesian Query Construction for Neural Network Models. NIPS 1994: 443-450 - 1993
- [c10]Gerhard Paass, Franz J. Kurfess:
Wissensverarbeitung mit neuronalen Netzen. KI 1993: 217-225 - 1992
- [c9]Gerhard Paass:
Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm. NIPS 1992: 196-203 - [c8]Gerhard Paass:
MESA: Maximum Entropy by Simulated Annealing. UAI 1992: 230-237 - 1991
- [c7]Gerhard Paass:
Probabilistic Default Reasoning Involving Continous Variables. ECSQARU 1991: 267-271 - [c6]Gerhard Paass:
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm. UAI 1991: 264-270 - 1990
- [c5]Gerhard Paass:
Probabilistic Default Reasoning. IPMU 1990: 76-85 - [c4]Gerhard Paass:
A Stochastic EM Learning Algorithm for Structured Probabilistic Neural Networks. ÖGAI 1990: 196-201 - [c3]Gerhard Paass:
Second order probabilities for uncertain and conflicting evidence. UAI 1990: 447-456 - 1989
- [c2]Gerhard Paass:
Bayesian Integration of Uncertain and Conflicting Evidence. GWAI 1989: 421-427 - 1985
- [c1]Gerhard Paass:
Anonymität von Individualdaten in statistischen Datenbanken. Datenschutz und Datensicherung 1985: 156-170
Parts in Books or Collections
- 2020
- [p5]Christian Bauckhage, Wolfgang Hübner, Ronny Hug, Gerhard Paaß, Stefan Rüping:
Grundlagen des Maschinellen Lernens. Handbuch der Künstlichen Intelligenz 2020: 429-508 - [p4]Christian Bauckhage, Wolfgang Hübner, Ronny Hug, Gerhard Paaß:
Tiefe neuronale Netze. Handbuch der Künstlichen Intelligenz 2020: 509-570 - [p3]Christian Bauckhage, Johannes Fürnkranz, Gerhard Paaß:
Vertrauenswürdiges, transparentes und robustesMaschinelles Lernen. Handbuch der Künstlichen Intelligenz 2020: 571-600 - 2014
- [p2]Günter Neumann, Gerhard Paaß, David van den Akker:
Linguistics to Structure Unstructured Information. Towards the Internet of Services 2014: 383-392 - 2012
- [p1]Gerhard Paaß, Iuliu Vasile Konya:
Machine Learning for Document Structure Recognition. Modeling, Learning, and Processing of Text Technological Data Structures 2012: 221-247
Editorship
- 2009
- [e1]Hsinchun Chen, Marc Dacier, Marie-Francine Moens, Gerhard Paass, Christopher C. Yang:
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-669-4 [contents]
Informal and Other Publications
- 2023
- [i4]Gerhard Paaß, Sven Giesselbach:
Foundation Models for Natural Language Processing - Pre-trained Language Models Integrating Media. CoRR abs/2302.08575 (2023) - 2013
- [i3]Gerhard Paaß:
MESA: Maximum Entropy by Simulated Annealing. CoRR abs/1303.5422 (2013) - [i2]Gerhard Paass:
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm. CoRR abs/1303.5737 (2013) - [i1]Gerhard Paaß:
Second Order Probabilities for Uncertain and Conflicting Evidence. CoRR abs/1304.1139 (2013)
Coauthor Index
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last updated on 2024-09-20 00:42 CEST by the dblp team
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