Formal concept refinement by deep cognitive machine learning

Concept generation and refinement is a process to generate and improve machine's knowledge base represented by a comprehensive set of formal concepts. An unsupervised algorithm for concept refinement is developed for autonomously upgrading and enhancing acquired concepts of knowledge in a cogni...

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Bibliographic Details
Published in2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCICC) pp. 71 - 78
Main Authors Zatarain, Omar A., Yingxu Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Algebra
algorithm
Cats
Cognition
cognitive computation
Cognitive machine learning
computational linguistics
concept refinement
Handheld computers
Knowledge based systems
knowledge learning
Printers
unsupervised learning
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Summary:Concept generation and refinement is a process to generate and improve machine's knowledge base represented by a comprehensive set of formal concepts. An unsupervised algorithm for concept refinement is developed for autonomously upgrading and enhancing acquired concepts of knowledge in a cognitive knowledge base built by cognitive robots and systems. The concept refinement algorithm is implemented based on a set of rules of concept algebra and semantic analyses. Experimental results demonstrate that cognitive machines can autonomously refine their knowledge by improving acquired concepts in a dynamic process mimicking human learning mechanisms in deep machine learning and cognitive computing.
DOI:10.1109/ICCI-CC.2017.8109732