Learning theory for higher-knowledge self-organization from experiences

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The purpose of this work is to establish the learning theory and the algorithm acquiring the general knowledge through experiences. The proposed method has a hierarchical structure consisting of a set of first learners and the second learner, in which the first learners estimate models of individual experiences and the second learner leans the learning result of the first learners. We found that the task is described as tensor equations, which can be solved by the higher-rank of self-organizing map (SOM). Furthermore, we developed tensor SOM based on the higher-rank of SOM.