标题： 基于核方法的覆盖分类 显示英文标题

标题： Classification Recouvrante Basée sur les Méthodes à Noyau

摘要： 重叠聚类问题是一个重要的学习问题，其中聚类不是互斥的，每个对象可能同时属于多个聚类。 本文提出了一种基于核的方法，利用Mercer核技术在高特征空间中生成重叠聚类，以提高输入模式的可分性。 所提出的方法称为OKM-K（基于重叠$k$-means的核方法），它扩展了OKM（重叠$k$-means）方法以生成重叠方案。 在重叠数据集上进行了实验，与OKM得到的结果相比，OKM-K得到的实验结果更优。 显示英文摘要

摘要： Overlapping clustering problem is an important learning issue in which clusters are not mutually exclusive and each object may belongs simultaneously to several clusters. This paper presents a kernel based method that produces overlapping clusters on a high feature space using mercer kernel techniques to improve separability of input patterns. The proposed method, called OKM-K(Overlapping $k$-means based kernel method), extends OKM (Overlapping $k$-means) method to produce overlapping schemes. Experiments are performed on overlapping dataset and empirical results obtained with OKM-K outperform results obtained with OKM.