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Learnability of Bipartite Ranking Functions
, 2005 The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We define a model of learnability for ranking functions in a particular setting of the ranking problem known as the bipartite ranking problem, and derive a ...
Shivani Agarwal 0001, Dan Roth 0001
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Synthesis of ranking functions via DNN
Neural Computing and Applications, 2021Tan Wang
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Ranking Functions over Labelings
Comma, 2018We study rankings over labelings as a generalization of traditional labeling-based semantics in abstract argumentation. Our approach is an alternative to recent developments on rankings over arguments. The formal basis is a qualitative abstraction of probability theory called ranking theory. We propose a fundamental property, called SCC stratification,
Tjitze Rienstra, Matthias Thimm
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LEG Networks for Ranking Functions
European Conference on Logics in Artificial Intelligence, 2014When using representations of plausibility for semantical frameworks, the storing capacity needed is usually exponentially in the number of variables. Therefore, network-based approaches that decompose the semantical space have proven to be fruitful in environments with probabilistic information. For applications where a more qualitative information is
Christian Eichhorn, G. Kern-Isberner
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Construct Weak Ranking Functions for Learning Linear Ranking Function
Asia Information Retrieval Symposium, 2011Many Learning to Rank models, which apply machine learning techniques to fuse weak ranking functions and enhance ranking performances, have been proposed for web search. However, most of the existing approaches only apply the Min --- Max normalization method to construct the weak ranking functions without considering the differences among the ranking ...
G. Hua +4 more
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Dominance-Based Ranking Functions for Interval-Valued Intuitionistic Fuzzy Sets
IEEE Transactions on Cybernetics, 2014Liang-Hsuan Chen
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Policy Iteration-Based Conditional Termination and Ranking Functions
International Conference on Verification, Model Checking and Abstract Interpretation, 2014 The final publication is available at link.springer.com.International audienceTermination analyzers generally synthesize ranking functions or relations, which represent checkable proofs of their results.
Damien Massé
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An Abstract Domain to Infer Ordinal-Valued Ranking Functions
European Symposium on Programming, 2014 International audienceThe traditional method for proving program termination consists in inferring a ranking function. In many cases (i.e. programs with unbounded non-determinism), a single ranking function over natural numbers is not sufficient.
Caterina Urban, A. Miné
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Proceedings of the VLDB Endowment, 2020
Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards ...
A. Gale, Amélie Marian
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Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards ...
A. Gale, Amélie Marian
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Revision by Comparison for Ranking Functions
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022Revision by Comparison (RbC) is a non-prioritized belief revision mechanism on epistemic states that specifies constraints on the plausibility of an input sentence via a designated reference sentence, allowing for kind of relative belief revision. In this paper, we make the strategy underlying RbC more explicit and transfer the mechanism together with ...
Meliha Sezgin, Gabriele Kern-Isberner
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