Fürnkranz, Johannes - Fachbereich Informatik, Technische Universität Darmstadt

• Technische Universitt Darmstadt Knowledge Engineering Group
• Inductive Rule Learning for Data and Web Mining
• TopDown Pruning in Relational Learning Johannes Furnkranz 1
• Pairwise Preference Learning and Ranking Johannes Furnkranz1
• Fossil: A Robust Relational Learner Johannes Furnkranz
• An Empirical Investigation of the Trade-Off Between Consistency and Coverage in Rule Learning Heuristics
• Label Ranking by Learning Pairwise Preferences
• A Case Study in Using Linguistic Phrases for Text Categorization on the WWW
• A Brief Introduction to Knowledge Discovery in Databases
• Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain
• A Tight Integration of Pruning and Learning (Extended Abstract)
• A Comparison of Techniques for Selecting and Combining Class Association Rules
• Link-Local Features for Hypertext Classification Herve Utard and Johannes Furnkranz
• More Efficient Windowing Johannes F
• An Analysis of Stopping and Filtering Criteria for Rule Johannes Furnkranz1
• Exploiting Structural Information for Text Classification on the WWW
• Inductive Logic Programming (A Short Introduction and a Thesis Abstract)
• A Unified Model for Multilabel Classification and Ranking
• Johannes Frnkranz TU Darmstadt, Knowledge Engineering Group
• Technische Universitt Darmstadt Knowledge Engineering Group
• On Minimizing the Position Error in Label Ranking
• Learning Preference Models from Data: On the Problem of Label Ranking
• Technische Universitt Darmstadt Knowledge Engineering Group
• Technische Universitt Darmstadt Knowledge Engineering Group
• Technische Universitt Darmstadt Knowledge Engineering Group
• From Local Patterns to Global Models: The LeGo Approach to Data Mining
• Technische Universitt Darmstadt Knowledge Engineering Group
• Technische Universitt Darmstadt Knowledge Engineering Group
• Pairwise Learning of Multilabel Classifications with Perceptrons
• Technische Universitt Darmstadt Knowledge Engineering Group
• Efficient Pairwise Classification Sang-Hyeun Park and Johannes Furnkranz
• Technische Universitt Darmstadt Knowledge Engineering Group
• Learning Label Preferences: Ranking Error Versus Position Error
• Combining Pairwise Classifiers with Stacking Petr Savicky and Johannes Furnkranz
• Learning to Use Operational Advice Johannes Furnkranz
• From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms
• Meta-Learning Rule Learning Heuristics Frederik Janssen and Johannes Furnkranz
• Technische Universitt Darmstadt Knowledge Engineering Group
• On Position Error and Label Ranking Through Iterated Choice Eyke Hullermeier
• Modeling Rule Precision Johannes Furnkranz
• Modeling Rule Precision Johannes Furnkranz
• Preference Learning Johannes Furnkranz, Eyke Hullermeier
• An Evaluation of Grading Classifiers Alexander K. Seewald and Johannes Furnkranz
• On Pairwise Naive Bayes Classifiers Jan-Nikolas Sulzmann1
• A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
• Digging for Peace: Using Machine Learning Methods for Assessing
• Pairwise Classification as an Ensemble Technique Johannes Furnkranz
• NoiseTolerant Windowing Johannes F
• Knowledge Discovery in Chess Databases: A Research Proposal Johannes F
• Round Robin Rule Learning Johannes Furnkranz JUFFI@OEFAI.AT
• Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain
• Recent Advances in Machine Learning and Game Playing