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- W2312610852 abstract "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." @default.
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- W2312610852 date "2014-04-10" @default.
- W2312610852 modified "2023-09-26" @default.
- W2312610852 title "Minimax Learning Rate for Multi-dividing Ontology Algorithm" @default.
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- W2312610852 doi "https://doi.org/10.12733/jics20103216" @default.
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