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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
Basic Concepts
Problem Statement
Prediction Tasks
Key Properties
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
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