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Statistical learning theory (SLT) based on random samples on probability space is considered as the best theory about small samples statistics learning at present and has become a new hot field in machine learning after neural networks. However, the theory can not handle the small samples statistical learning problems on set-valued probability space which widely exists in real world. In this paper,...
In this paper, the idea of the structural risk minimization (SRM) on credibility space is presented; two theorems are proven to answer two questions: is the structural risk minimization principle consistent on credibility space? (Does the risk for the functions chosen according to this principle converge to the smallest possible risk for the set S with increasing amount of observations?) What is the...
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