[1]朱林立,华钢,高炜.本体学习算法的两类LOO一致稳定性和广义界[J].智能系统学报,2022,17(3):471-479.[doi:10.11992/tis.202101015]
ZHU Linli,HUA Gang,GAO Wei.Two classes of LOO uniform stability and generalization bounds of ontology learning algorithm[J].CAAI Transactions on Intelligent Systems,2022,17(3):471-479.[doi:10.11992/tis.202101015]
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ZHU Linli,HUA Gang,GAO Wei.Two classes of LOO uniform stability and generalization bounds of ontology learning algorithm[J].CAAI Transactions on Intelligent Systems,2022,17(3):471-479.[doi:10.11992/tis.202101015]
本体学习算法的两类LOO一致稳定性和广义界
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第3期
页码:
471-479
栏目:
学术论文—机器学习
出版日期:
2022-05-05
- Title:
- Two classes of LOO uniform stability and generalization bounds of ontology learning algorithm
- Keywords:
- ontology; machine learning; stability; generalized bound; ontology data-dependent function; ontology sample dependent hypothesis set; Rademacher complexity; empirical Rademacher complexity
- 分类号:
- TP391
- 摘要:
- 近年来，随着本体研究的深入，各类机器学习方法被尝试应用于本体相似度计算和本体映射获取。稳定性是本体学习算法的必要条件，它从本质上体现了算法的可用性，即要求本体学习算法的最优解不会受到本体样本的小幅度调整而发生大的变化。本文研究了删除一个本体样本点的条件下，对本体学习算法的期望误差与经验误差的差值产生的影响。分别在本体学习算法一致稳定和假设空间一致稳定两种不同的框架下，利用统计学习理论的技巧，得到对应广义界的上界估计。
- Abstract:
- Recently, with deepening ontology research, efforts have been made to apply various machine-learning methods to ontology similarity calculations and mapping acquisitions. Stability is a necessary condition for ontology-learning algorithms. It requires that the optimal solution of the algorithm does not undergo major changes due to small adjustments to the ontology samples; thus, it essentially reflects the usability of the algorithm. In this study, we investigated the effect of deleting an ontology sample point on the difference between the expected and empirical errors of the ontology-learning algorithm. In two settings of the ontology-learning algorithm—uniform stability and hypothetical space uniform stability—obtained using statistical learning theory, the corresponding upper bound estimates of generalized bounds are determined.
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备注/Memo
收稿日期:2021-01-09。
基金项目:国家自然科学基金项目(51574232).
作者简介:朱林立，高级工程师，博士，主要研究方向为人工智能、机器学习;华钢，教授，博士，主要研究方向为物联网、矿山监控与监管、智能信息处理;高炜，教授，博士，主要研究方向为图论、人工智能和统计学习理论
通讯作者:朱林立.E-mail：zhulinli@jsut.edu.cn
更新日期/Last Update:
1900-01-01