Dyad ranking with generalized Plackett-Luce models / Dirk Schäfer. Paderborn, 2018
Inhalt
- Introduction
- Preference Learning
- Toward a new Problem Setting
- Research Questions
- Outline of the Thesis
- Contributions
- Dyad Ranking
- Related Settings and Methods
- Label Ranking
- Object Ranking
- Learning To Rank
- Collaborative Filtering and Ranking
- Conditional Ranking
- Dyadic Prediction
- Zero-Shot Learning
- Generalized Plackett–Luce Models for Dyad Ranking
- Plackett-Luce Model
- Joint-Feature Plackett-Luce Model
- Bilinear Plackett–Luce Model
- Plackett-Luce Networks
- Connections between the Models
- Multidimensional Unfolding and Scaling with Dyad Ranking
- The Unfolding Problem
- Dyadic Unfolding
- Dyadic Unfolding with SMACOF
- Dyadic Multidimensional Scaling
- Related Visualization Approaches for Ranking Data
- Preference-based Reinforcement Learning using Dyad Ranking
- Reinforcement Learning
- Preference-based Reinforcement Learning
- PBRL using Dyad Ranking
- Standard Benchmarks
- Experiments on Dyad Ranking
- Comparison with Label Ranking
- Configuration Learning for Genetic Algorithms
- Multi-label Ranking of Musical Emotions
- Configuration of Image Processing Pipelines
- Similarity Learning on Tagged Images
- Conclusion
- Appendix