Theoretical Perspective of Multi-Dividing Ontology Learning Trick in Two-Sample Setting

The multi-dividing ontology learning framework has been proven to have a higher efficiency for tree-structured ontology learning, and in this work, we consider a special setting of this learning framework in which ontology sample set for each rate is divided into two groups. This setting can be rega...

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Bibliographic Details
Main Authors: Linli Zhu, Gang Hua
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9276418/