Gerhard Paass

Gerhard Paaß: MESA: Maximum Entropy by Simulated Annealing. CoRR abs/1303.5422 (2013)

Gerhard Paass: Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm. CoRR abs/1303.5737 (2013)

Gerhard Paaß: Second Order Probabilities for Uncertain and Conflicting Evidence. CoRR abs/1304.1139 (2013)

Anja Pilz, Gerhard Paaß: Collective Search for Concept Disambiguation. COLING 2012: 2243-2258

Gerhard Paaß, André Bergholz, Anja Pilz: A knowledge-extraction approach to identify and present verbatim quotes in free text. I-KNOW 2012: 31

Donald E. Brown, Fazel Famili, Gerhard Paass, Kate Smith-Miles, Lyn C. Thomas, Richard Weber, Ricardo A. Baeza-Yates, Cristián Bravo, Gaston L'Huillier, Sebastián Maldonado: Future trends in business analytics and optimization. Intell. Data Anal. 15(6): 1001-1017 (2011)

Anja Pilz, Gerhard Paaß: From names to entities using thematic context distance. CIKM 2011: 857-866

André Bergholz, Jan De Beer, Sebastian Glahn, Marie-Francine Moens, Gerhard Paaß, Siehyun Strobel: New filtering approaches for phishing email. Journal of Computer Security 18(1): 7-35 (2010)

Frank Reichartz, Hannes Korte, Gerhard Paass: Semantic relation extraction with kernels over typed dependency trees. KDD 2010: 773-782

Frank Reichartz, Hannes Korte, Gerhard Paass: Composite Kernels For Relation Extraction. ACL/IJCNLP (Short Papers) 2009: 365-368

Tamás Horváth, Gerhard Paass, Frank Reichartz, Stefan Wrobel: A Logic-Based Approach to Relation Extraction from Texts. ILP 2009: 34-48

Frank Reichartz, Hannes Korte, Gerhard Paass: Dependency Tree Kernels for Relation Extraction from Natural Language Text. ECML/PKDD (2) 2009: 270-285

Gerhard Paaß, Frank Reichartz: Exploiting Semantic Constraints for Estimating Supersenses with CRFs. SDM 2009: 485-496

Gerhard Paass, Anja Pilz, Jochen Schwenninger: Named Entity Recognition of Spoken Documents Using Subword Units. ICSC 2009: 529-534

Hsinchun Chen, Marc Dacier, Marie-Francine Moens, Gerhard Paass, Christopher C. Yang (Eds.): Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, Paris, France, June 28, 2009. ACM 2009, isbn 978-1-60558-669-4

André Bergholz, Jeong Ho Chang, Gerhard Paass, Frank Reichartz, Siehyun Strobel: Improved Phishing Detection using Model-Based Features. CEAS 2008

André Bergholz, Gerhard Paass, Frank Reichartz, Siehyun Strobel, Marie-Francine Moens, Brian Witten: Detecting Known and New Salting Tricks in Unwanted Emails. CEAS 2008

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

Edda Leopold, Jörg Kindermann, Gerhard Paaß: Analysis of E-Discussions Using Classifier Induced Semantic Spaces. LDV Forum 22(1): 21-27 (2007)

Andreas Hotho, Andreas Nürnberger, Gerhard Paass: A Brief Survey of Text Mining. LDV Forum 20(1): 19-62 (2005)

Gerhard Paaß, Hugo de Vries: Evaluating the Performance of Text Mining Systems on Real-world Press Archives. GfKl 2005: 414-421

René Cavet, Stephan Volmer, Edda Leopold, Jörg Kindermann, Gerhard Paaß: Revealing the connoted visual code: a new approach to video classification. Computers & Graphics 28(3): 361-369 (2004)

Gerhard Paaß, Jörg Kindermann, Edda Leopold: Learning Prototype Ontologies by Hierachical Latent Semantic Analysis. LWA 2004: 193-205

Joachim Diederich, Jörg Kindermann, Edda Leopold, Gerhard Paass: Authorship Attribution with Support Vector Machines. Appl. Intell. 19(1-2): 109-123 (2003)

Gerhard Paass, Jörg Kindermann: Bayesian regression mixtures of experts for geo-referenced data. Intell. Data Anal. 7(6): 567-582 (2003)

Martha 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

Gerhard Paass, Edda Leopold, Martha Larson, Jörg Kindermann, Stefan Eickeler: SVM Classification Using Sequences of Phonemes and Syllables. PKDD 2002: 373-384

Jörg Kindermann, Gerhard Paass, Edda Leopold: Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization. PKDD 2001: 266-276

Gerhard Paass, Jörg Kindermann: Multistep Sequential Exploration of Growing Bayesian Classification Models. IJCNN (3) 2000: 566-574

Gerhard Paass, Jörg Kindermann: Transparency and Predicive Power: Explaining Complex Classification Models. PKDD 2000: 558-565

Gerhard Paass, Jörg Kindermann: Klassifikation mit Populationen von heterogenen Modellen. KI 12(2): 56-65 (1998)

Gerhard Paass, Jörg Kindermann: Bayesian Classification Trees with Overlapping Leaves Applied to Credit-Scoring. PAKDD 1998: 234-245

Jörg Kindermann, Gerhard Paass: Model Switching for Bayesian Classification Trees with Soft Splits. PKDD 1998: 148-157

Heinz Mühlenbein, Gerhard Paass: From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. PPSN 1996: 178-187

Joachim Diederich, Don Simonetta, Gerhard Paass: Estimating Blue-Green Algae Counts with Neurocomputing Techniques. IEA/AIE 1995: 127-131

Knut Möller, Gerhard Paass: Künstliche Neuronale Netze: eine Bestandsaufnahme. KI 8(4): 37-61 (1994)

Gerhard Paass, Jörg Kindermann: Bayesian Query Construction for Neural Network Models. NIPS 1994: 443-450

Alexander Linden, Gerhard Paass: Bericht von der Konferenz ``Neural Information Processing Szstems - Natural and Synthetic'' (NIPS) 1992. KI 7(3): 26-29 (1993)

Gerhard Paass, Franz J. Kurfess: Wissensverarbeitung mit neuronalen Netzen. KI 1993: 217-225

Jochen Heinsohn, Gerhard Paass: European Conference on Symbolic and Quantitative Approaches for Uncertainty. KI 6(2): 42 (1992)

Gerhard Paass: Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm. NIPS 1992: 196-203

Gerhard Paass: MESA: Maximum Entropy by Simulated Annealing. UAI 1992: 230-237

Gerhard Paass: Probabilistic Default Reasoning Involving Continous Variables. ECSQARU 1991: 267-271

Gerhard Paass: Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm. UAI 1991: 264-270

Gerhard Paass: Probabilistic Default Reasoning. IPMU 1990: 76-85

Gerhard Paass: A Stochastic EM Learning Algorithm for Structured Probabilistic Neural Networks. ÖGAI 1990: 196-201

Gerhard Paass: Second order probabilities for uncertain and conflicting evidence. UAI 1990: 447-456

Gerhard Paass: Bayesian Integration of Uncertain and Conflicting Evidence. GWAI 1989: 421-427

Gerhard Paass: Anonymität von Individualdaten in statistischen Datenbanken. Datenschutz und Datensicherung 1985: 156-170