Gerhard Paass

Publication Activity (10 Years)

Years Active: 1985-2013
Publications (10 Years): 0

Top Topics

Multilayer Perceptron

Rule Extraction

Trained Neural Networks

Top Venues

Publications

Gerhard Paass
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm CoRR (2013)
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) (2011)
Frank Reichartz, Hannes Korte, Gerhard Paass
Semantic relation extraction with kernels over typed dependency trees. KDD (2010)
Tamás Horváth, Gerhard Paass, Frank Reichartz, Stefan Wrobel
A Logic-Based Approach to Relation Extraction from Texts. ILP (2009)
Frank Reichartz, Hannes Korte, Gerhard Paass
Composite Kernels For Relation Extraction. ACL/IJCNLP (2) (2009)
Frank Reichartz, Hannes Korte, Gerhard Paass
Dependency Tree Kernels for Relation Extraction from Natural Language Text. ECML/PKDD (2) (2009)
Gerhard Paass, Anja Pilz, Jochen Schwenninger
Named Entity Recognition of Spoken Documents Using Subword Units. ICSC (2009)
André Bergholz, Jeong Ho Chang, Gerhard Paass, Frank Reichartz, Siehyun Strobel
Improved Phishing Detection using Model-Based Features. 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)
André Bergholz, Gerhard Paass, Frank Reichartz, Siehyun Strobel, Marie-Francine Moens, Brian Witten
Detecting Known and New Salting Tricks in Unwanted Emails. CEAS (2008)
Andreas Hotho, Andreas Nürnberger, Gerhard Paass
A Brief Survey of Text Mining. LDV Forum 20 (1) (2005)
Joachim Diederich, Jörg Kindermann, Edda Leopold, Gerhard Paass
Authorship Attribution with Support Vector Machines. Appl. Intell. 19 (1-2) (2003)
Gerhard Paass, Jörg Kindermann
Bayesian regression mixtures of experts for geo-referenced data. Intell. Data Anal. 7 (6) (2003)
Gerhard Paass, Edda Leopold, Martha A. Larson, Jörg Kindermann, Stefan Eickeler
SVM Classification Using Sequences of Phonemes and Syllables. PKDD (2002)
Jörg Kindermann, Gerhard Paass, Edda Leopold
Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization. PKDD (2001)
Gerhard Paass, Jörg Kindermann
Transparency and Predicive Power: Explaining Complex Classification Models. PKDD (2000)
Gerhard Paass, Jörg Kindermann
Multistep Sequential Exploration of Growing Bayesian Classification Models. IJCNN (3) (2000)
Byoung-Tak Zhang, Gerhard Paass, Heinz Mühlenbein
Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains. CEC (2000)
Gerhard Paass, Jörg Kindermann
Klassifikation mit Populationen von heterogenen Modellen. Künstliche Intell. 12 (2) (1998)
Gerhard Paass, Jörg Kindermann
Bayesian Classification Trees with Overlapping Leaves Applied to Credit-Scoring. PAKDD (1998)
Jörg Kindermann, Gerhard Paass
Model Switching for Bayesian Classification Trees with Soft Splits. PKDD (1998)
Heinz Mühlenbein, Gerhard Paass
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. PPSN (1996)
Joachim Diederich, Don Simonetta, Gerhard Paass
Estimating Blue-Green Algae Counts with Neurocomputing Techniques. IEA/AIE (1995)
Gerhard Paass, Jörg Kindermann
Bayesian Query Construction for Neural Network Models. NIPS (1994)
Knut Möller, Gerhard Paass
Künstliche Neuronale Netze: eine Bestandsaufnahme. Künstliche Intell. 8 (4) (1994)
Gerhard Paass, Franz J. Kurfess
Wissensverarbeitung mit neuronalen Netzen. KI (1993)
Alexander Linden, Gerhard Paass
Bericht von der Konferenz "Neural Information Processing Szstems - Natural and Synthetic" (NIPS) 1992. Künstliche Intell. 7 (3) (1993)
Jochen Heinsohn, Gerhard Paass
European Conference on Symbolic and Quantitative Approaches for Uncertainty. Künstliche Intell. 6 (2) (1992)
Gerhard Paass
Assessing and Improving Neural Network Predictions by the Bootstrap Algorithm. NIPS (1992)
Gerhard Paass
MESA: Maximum Entropy by Simulated Annealing. UAI (1992)
Gerhard Paass
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm. UAI (1991)
Gerhard Paass
Probabilistic Default Reasoning Involving Continous Variables. ECSQARU (1991)
Gerhard Paass
Probabilistic Default Reasoning. IPMU (1990)
Gerhard Paass
A Stochastic EM Learning Algorithm for Structured Probabilistic Neural Networks. ÖGAI (1990)
Gerhard Paass
Second order probabilities for uncertain and conflicting evidence. UAI (1990)
Gerhard Paass
Bayesian Integration of Uncertain and Conflicting Evidence. GWAI (1989)
Gerhard Paass
Anonymität von Individualdaten in statistischen Datenbanken. Datenschutz und Datensicherung (1985)