%0 Journal Article
%T 多示例学习的示例层次覆盖算法<br>Multi-instance Learning with Instance-Level Covering Algorithm
%A 董露露
%A 谢飞
%A 章程
%J 数据采集与处理
%D 2018
%R 10.16337/j.1004-9037.2018.02.019
%X 在多示例学习（Multi-instance learning，MIL）中，核心示例对于包类别的预测具有重要作用。若两个示例周围分布不同数量的同类示例，则这两个示例的代表程度不同。为了从包中选出最具有代表性的示例组成核心示例集，提高分类精度，本文提出多示例学习的示例层次覆盖算法（Multi-instance learning with instance_level covering algorithm，MILICA）。该算法首先利用最大Hausdorff距离和覆盖算法构建初始核心示例集，然后通过覆盖算法和反验证获得最终的核心示例集和各覆盖包含的示例数，最后使用相似函数将包转为单示例。在两类数据集和多类图像数据集上的实验证明，MILICA算法具有较好的分类性能。<br>In multi-instance learning, the core instances play an important role on the prediction of bags' label. And if two instances have different numbers of instances with the same category around them, they have different levels of representative. In order to improve the classification accuracy, multi-instance learning with instance-level covering algorithm (MILICA) is proposed by which we could select the most representative instances to form the core instance set. Firstly, with the max Hausdorffdistance and the covering algorithm, the initial core instance set is constructed. Then, the final core instance set and the number of instances in a cover are obtained. Finally, a similarity measure function is used to convert a bag into a single sample for classification. Experimental results on two-category datasets and multi-category image datasets demonstrate that the proposed MILICA method has perfect classification capability.
%K 多示例学习
%K 覆盖算法
%K 核心示例集
%K 相似度函数&lt
%K br&gt
%K multi-instance learning
%K covering algorithm
%K core instance set
%K similarity measure function
%U http://sjcj.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=20180219&flag=1