资源分类
》 资源 》论文》The Complexity of Learning Separable ceteris paribus Preferences

The Complexity of Learning Separable ceteris paribus Preferences

2019-11-15 | |  82 |   36 |   0

Abstract We address the problem of learning preference relations on multi-attribute (or combinatorial) domains. We do so by making a very simple hypothesis about the dependence structure between attributes that the preference relation enjoys, namely separability (no preferential dependencies between attributes). Given a set of examples consisting of comparisons between alternatives, we want to output a separable CP-net, consisting of local preferences on each of the attributes, that fifits the examples. We consider three forms of compatibility between a CP-net and a set of examples, and for each of them we give useful characterizations as well as complexity results.

上一篇：Minimal Module Extraction from DL-Lite Ontologies Using QBF Solvers

下一篇：Combining RCC-8 with Qualitative Direction Calculi: Algorithms and Complexity

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...