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Discovery and Ranking of Functional Dependencies
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Computing the functional dependencies that hold on a given data set is one of the most important problems in data profiling. Utilizing new data structures and original techniques for the dynamic computation of stripped partitions, we devise a new hybridization strategy that outperforms the best algorithms in terms of efficiency, column-, and row ...
Ziheng Wei, Sebastian Link
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A contest success function for rankings [PDF]
Social Choice and Welfare, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Construct Weak Ranking Functions for Learning Linear Ranking Function
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 ...
Guichun Hua +4 more
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LEG Networks for Ranking Functions
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 0001 +1 more
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Rank Functions for Stable Diagrams
Siberian Advances in Mathematics, 2020Summary: Let \(D\) be the diagram of a sufficiently homogeneous model. For types that are realized in this model, we introduce certain rank functions and prove the following assertions: (1) If, for each type, the rank is less than \(\infty\) then the diagram is stable; (2) if the diagram \(D\) is stable then the set of non-algebraic types of rank less ...
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Learnability of Bipartite Ranking Functions
2005The 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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Generalized subdifferentials of the rank function
Optimization Letters, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Ranking Functions over Labelings
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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Ranking functions for size-change termination
ACM Transactions on Programming Languages and Systems, 2009This article explains how to construct a ranking function for any program that is proved terminating by size-change analysis . The “principle of size-change termination” for a first-order functional language with well-ordered data is intuitive: A program terminates on all inputs, if
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Aggregation of Individual Rankings Through Fusion Functions: Criticism and Optimality Analysis
IEEE Transactions on Fuzzy Systems, 2022Maria Jesus Campion +2 more
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