Gao Wei, Zhu Linli
School of Information and Technology, Yunnan Normal University, Kunming 650500, China.
School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Comput Intell Neurosci. 2014;2014:438291. doi: 10.1155/2014/438291. Epub 2014 Oct 29.
The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting.
鉴于梯度学习模型在统计学、数据降维及其他特定领域的应用前景广阔，它一直备受关注。在本文中，我们提出了一种用于多划分环境下本体相似度测量和本体映射的新梯度学习模型。借助假设空间和本体划分算子技巧给出了该环境下的样本误差。最后，在植物和仿人机器人领域进行的两个实验验证了新计算模型在多划分环境下用于本体相似度测量和本体映射应用的有效性。
