Query-based learning of acyclic conditional preference networks from noisy data
Texte intégral
Figure
Documents relatifs
Comme nous nous int´eressons aux pr´ef´erences d’un groupe d’utilisateurs, nous allons ajouter des probabilit´es dans les tables de pr´ef´erences lo- cales afin de construire
We have described in this paper how it is possible to learn a probabilistic representation of the preferences of a group of users over a combinatorial domain, or how we can fine-
Moreover, the possibilistic representation is expressive (see [10] for an introductory survey), and can capture partial orders thanks to the use of symbolic weights, without
By contrast, acyclic CP-nets are what is called attribute-efficiently learnable when both equivalence queries and membership queries are available: we indeed provide a
The lower bound is proved by examining the decision tree of the deterministic query scheme and exhibiting an input for which the number of queries asked is high.. More precisely,
Dynamis of preferene systems We have onsidered xed aeptane graph and preferene lists.
Since it is easy, and reasonable, to further impose that the learned CP-net has a digraph with a small degree k (see the experiments), and hence that m is in O(n k ), we finally get
The class C TREE is attribute-efficiently learnable from equivalence and membership queries over arbitrary out- come pairs: any concept ≻ in this class can be identified in