• Corpus ID: 9467525

The Complexity of Learning Acyclic CP-Nets

@inproceedings{Alanazi2016TheCO,
  title={The Complexity of Learning Acyclic CP-Nets},
  author={Eisa A. Alanazi and Malek Mouhoub and Sandra Zilles},
  booktitle={IJCAI},
  year={2016}
}
Learning of user preferences has become a core issue in AI research. For example, recent studies investigate learning of Conditional Preference Networks (CP-nets) from partial information. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of CP-net classes, it is helpful to calculate certain learning-theoretic information complexity parameters. This paper provides theoretical justification for exact values (or in some cases bounds) of… 
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This article focuses on the frequently studied case of learning from so-called swap examples, which express preferences among objects that differ in only one attribute, and presents bounds on or exact values of some well-studied information complexity parameters, namely the VC dimension, the teaching dimension, and the recursive teaching dimension for classes of acyclic CP-nets.
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TLDR
This paper determines bounds on or exact values of some of the most central information complexity parameters, namely the VC dimension, the (recursive) teaching dimension), the self-directed learning complexity, and the optimal mistake bound, for classes of acyclic CP-nets.
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TLDR
This paper proposes a new, efficient, and robust query-based learning algorithm for acyclic CP-nets that takes into account the incoherences in the user’s preferences or in noisy data by searching in a principled way the variables that condition the other ones.
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TLDR
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TLDR
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TLDR
A class of the parent vertices in a ring CP-nets firstly and then gives corresponding algorithm, respectively, based on FVS and FAS based on feedback vertex set and feedback arc set are defined.
Structure Learning of Conditional Preference Networks Based on Dependent Degree of Attributes From Preference Database
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This paper provides theoretical support for the use of a conditional independent test for learning the structure of CP-nets and proposes the dependent degree to calculate the dependency relationship among attributes.
Summarizing Conditional Preference Networks
TLDR
This thesis proposes an approach to aggregate the preferences of multiple users via a single CP-net, while minimizing disagreement with individual users, and presents two algorithms that assume all the input CP-nets are separable.
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