广东工业大学学报2024，Vol.41Issue(2)：101-107,7.DOI:10.12052/gdutxb.230001
基于多核学习的单分类多示例学习算法
Multiple-kernel One-class Multiple-instance Learning Algorithm
摘要
Abstract
By introducing multiple-kernel into one-class multiple-instance learning,this paper proposes a novel multiple-kernel one-class multiple-instance learning based on support vector data description,which aims to solve the problem of multiple-kernel learning of multiple-instance data with a relatively complex distribution structure in practical applications.This algorithm maps multiple-instance data into the feature space through different multiple-kernel functions,and constructs a spherical classifier by using support vector data description algorithm.To iteratively optimize the proposed algorithm adopts an iterative optimization framework,we first initialize the instances in positive bags as positive,and optimize the objective function to build up the classifier.Then,the labels of the positive instances in each bag are updated according to the classifier obtained in the previous step.The experimental results on the Corel,VOC 2007 and Messidor datasets show that the proposed algorithm achieves significantly better classification performance than state-of-the-art methods,demonstrating its feasibility and effectiveness.关键词
多核学习/单分类/支持向量数据描述/多示例学习Key words
multiple-kernel learning/one-class classification/support vector data description/multiple-instance learning分类
信息技术与安全科学引用本文复制引用
古慧敏,肖燕珊,刘波..基于多核学习的单分类多示例学习算法[J].广东工业大学学报,2024,41(2):101-107,7.基金项目
国家自然科学基金资助项目(61876044,62076074) （61876044,62076074）
