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有噪条件下基于正则化模型的本体学习算法 被引量：6

Ontology learning algorithm based on regularization model under noise condition
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摘要 对原有的正则化本体模型加以改进,分别从再生核希尔伯特空间及线性空间Rm出发,得到有噪或半监督学习框架下的新正则化求解模型,将计算模型的注意力集中到假设空间的设置上.实验表明,新算法对特定的应用领域具有较高的效率. The regularization ontology model is improved by using the reproducing kernel Hilbert space and linear space of Rm ,and a new regularization model in semi-supervised learning setting is obtained.This new model focuses on the designing of hypothesis space.Two experiments show that the new algorithm for specific applications has higher efficiency.
作者 朱林立 吴访升 叶飞跃 高炜
机构地区 江苏理工学院计算机工程学院 云南师范大学信息学院
出处 《西北师范大学学报（自然科学版）》 CAS 北大核心 2014年第6期41-45,共5页 Journal of Northwest Normal University(Natural Science)
基金 国家自然科学基金资助项目(61142007) 江苏省高校自然科学研究项目(10KJD520002)
关键词 本体 相似度 本体映射 再生核希尔伯特空间 ontology similarity ontology mapping RKHS
分类号 TP393.092 [自动化与计算机技术—计算机应用技术]
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参考文献17

二级参考文献88

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共引文献56

同被引文献37

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引证文献6

二级引证文献5

西北师范大学学报（自然科学版）
2014年 第6期

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