Minimax Learning Rate for Multi-dividing Ontology Algorithm
Wei Dong Gao · Journal of Information and Computational Science · 2014
As an important data structure model, ontology has become one of the core contents in information science. Multi-dividing ontology algorithm combines the advantages of graph structure and learning algorithms proved to have high efficiency. In this paper, we investigate some theoretical problems of ontology algorithm in multi-dividing setting. The relationship between two versions of low noise assumptions is established. The risk excess and Lq-error are given. Specifically, the upper bound and lower bound minimax learning rate are obtained based on assumptions we describe in Section 2.