一种从数据中学习分级关系的基于核的框架
A kernel-based framework for learning graded relations from data
AI总结：
针对现有关系学习方法多处理清晰关系、难以适配实际分级关系的问题，提出一种基于核的通用框架，可兼容清晰与分级关系、建模多类分级关系，搭建模糊集与机器学习的关联，经实验验证有效。
摘要：
受生物信息学、信息检索、社交网络分析等领域大量潜在应用的推动，数据对象对之间的关系推断问题近期在机器学习社区得到了广泛研究。目前的方法通常采用包含清晰关系的数据集，以便使用标准分类方法。但在实际应用中，对象间的相似性、偏好等关系往往以分级形式表达。本文提出一种通用的基于核的关系学习框架，该框架扩展了现有方法，可同时处理清晰关系与分级关系；同时统一了现有方法，能够建模多种类型的分级关系，包括对称关系与互反关系。该框架在模糊集理论与机器学习的最新进展之间建立了重要关联。通过在合成数据与真实世界数据上开展的多组实验，验证了其有效性。
英文摘要：
Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
