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- W3211998900 abstract "Concept modeling and learning have been important research topics in artificial intelligence and knowledge discovery. This paper studies a hierarchical concept learning method that requires a small amount of data to achieve competitive performances. The method starts from a set of fuzzy prototypes called Fuzzy Semantic Cells (FSCs). As a result of FSC parameter optimization, it creates a hierarchical structure of data–prototype–concept. Experiments are conducted to demonstrate the effectiveness of our approach in a classification problem. In particular, when faced with limited training data, our proposed method is comparable with traditional techniques in terms of robustness and generalization ability." @default.
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- W3211998900 date "2021-11-13" @default.
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- W3211998900 title "Hierarchical Concept Learning by Fuzzy Semantic Cells" @default.
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- W3211998900 doi "https://doi.org/10.3390/app112210723" @default.
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