- Possibilistic Instance-Based Learning Eyke Hullermeier
- Comparison of Ranking Procedures in Pairwise Preference Learning
- A Notion of Comparative Probabilistic Entropy based on the Possibilistic Specificity Ordering
- A Unified Model for Multilabel Classification and Ranking
- Comparing Probability Measures Using Possibility Theory: A Notion of Relative
- Online Clustering of Parallel Data Streams Jurgen Beringer and Eyke Hullermeier
- A Note on Quality Measures for Fuzzy Association Rules
- A Systematic Approach to the Assessment of Fuzzy Association Rules
- Learning from Ambiguously Labeled Examples Eyke Hullermeier and Jurgen Beringer
- Learning Label Preferences: Ranking Error versus Position Error
- Cho-k-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning
- Fuzzy Methods in Machine Learning and Data Mining: Status and Prospects
- Ranking by Pairwise Comparison: A Note on Risk Minimization
- sterreichisches Forschungsinstitut fr / Austrian Research Institute for /
- Efficient Similarity Search in Protein Structure Databases: Improving Clique-Detection through Clique Hashing
- Pairwise Preference Learning and Ranking Johannes Furnkranz
- Learning from Ambiguously Labeled Examples Eyke Hullermeier and Jurgen Beringer
- Instance-Based Prediction with Guaranteed Confidence Eyke Hullermeier1
- Preference Learning Johannes Furnkranz, Eyke Hullermeier
- Instance-Based Learning of Credible Label Sets Eyke Hullermeier
- Mining Gradual Dependencies based on Fuzzy Rank Correlation
- On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure
- Label Ranking by Learning Pairwise Preferences
- FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers
- Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures
- Decision Tree and Instance-Based Learning for Label Ranking Weiwei Cheng cheng@mathematik.uni-marburg.de
- Label Ranking in Case-Based Reasoning Klaus Brinker and Eyke Hullermeier
- Is an Ordinal Class Structure Useful in Classifier Learning?
- Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning
- Choquistic Regression: Generalizing Logistic Regression Using the Choquet Integral
- Superposition and Alignment of Labeled Point Clouds Thomas Fober, Serghei Glinca, Gerhard Klebe and Eyke Hullermeier
- A Fuzzy Variant of the Rand Index for Comparing Clustering Structures
- Multilabel Classification via Calibrated Label Ranking Johannes Furnkranz
- Bipartite Ranking through Minimization of Univariate Loss Wojciech Kotlowski1,3
- Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions
- Fuzzy-Relational Classification: Combining Pairwise Decomposition Techniques with Fuzzy Preference Modeling
- Learning Monotone Nonlinear Models using the Choquet Integral
- Comparing Methods for Knowledge-Driven and Data-Driven Fuzzy Modeling: A Case Study in Textile Industry
- Supplementary Material to Evolutionary Construction of Multiple Graph Alignments for
- A Note on Quality Measures for Fuzzy Association Rules
- Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
- Combining Predictions in Pairwise Classification: An Optimal Adaptive Voting
- FURIA: An Algorithm For Unordered Fuzzy Rule Jens Hhn and Eyke Hllermeier
- Combining Instance-Based Learning and Logistic Regression
- Similarity Measures for Protein Structures based on Fuzzy Histogram Comparison
- Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection
- Why Fuzzy Decision Trees are Good Rankers Eyke Hullermeier
- Evolutionary Construction of Multiple Graph Alignments for the
- Preference Learning: An Introduction Johannes Furnkranz1
- SEGA: Semi-Global Graph Alignment for Structure-based Protein Comparison
- Efficient Instance-Based Learning on Data Streams Jurgen Beringer
- Credible Case-Based Inference Using Similarity Eyke Hullermeier
- In Defense of Fuzzy Association Analysis Eyke Hullermeier and Yu Yi
- Learning Valued Preference Structures for Solving Classification Problems
- A Proof of Theorem 1 Lemma 1: Let si, i = 1 . . . m, be real numbers such that 0 s1 s2 . . . sm.
- Fuzzy Clustering of Parallel Data Streams Jurgen Beringer and Eyke Hullermeier
- Graded Multilabel Classification: The Ordinal Case Weiwei Cheng1
- Case-based Multilabel Ranking Klaus Brinker and Eyke Hullermeier
- Graph-Kernels for the Comparative Analysis of Protein Active Sites
- Top-Down Induction of Fuzzy Pattern Trees Robin Senge and Eyke Hullermeier
- Preference-based CBR: First Steps Toward a Methodological Framework
- Pattern Trees for Regression and Fuzzy Systems Modeling
- Learning Similarity Functions from Qualitative Feedback
- Preference-Based CBR: First Steps Toward a Methodological Framework
- Learning Complexity-Bounded Rule-Based Classifiers by Combining Association Analysis and Genetic Algorithms
- International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 0, No. 0 (1993) 000|000
- On Pairwise Naive Bayes Classifiers Jan-Nikolas Sulzmann1
- Adaptive Optimization of the Number of Clusters in Fuzzy Clustering
- Fuzzy Sets in Machine Learning and Data Eyke Hullermeier
- Label Ranking Methods based on the Plackett-Luce Model Weiwei Cheng1
- Predicting Partial Orders: Ranking with Abstention
- Fuzzy Operator Trees for Modeling Rating Functions
- Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules
- Case-based Label Ranking Klaus Brinker1
- International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 7, No. 5 (1999) 439~1
- Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of
- Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
- On Minimizing the Position Error in Label Ranking
- On Label Dependence in Multi-Label Classification Krzysztof Dembczynski1,2
