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Analogy-Based Preference Learning with Kernels
Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning.
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
We consider the problem of statistical inference for ranking data, specifically rank aggregation, under the assumption that samples are incomplete in the sense of not comprising all choice alternatives.
Learning to Rank based on Analogical Reasoning
In this paper, we propose a new approach to object ranking based on principles of analogical reasoning.
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