Corpus ID: 236318535

A local approach to parameter space reduction for regression and classification tasks

  title={A local approach to parameter space reduction for regression and classification tasks},
  author={Francesco Romor and Marco Tezzele and Gianluigi Rozza},
Frequently, the parameter space, chosen for shape design or other applications that involve the definition of a surrogate model, present subdomains where the objective function of interest is highly regular or well behaved. So, it could be approximated more accurately if restricted to those subdomains and studied separately. The drawback of this approach is the possible scarsity of data in some applications, but in those, where a quantity of data, moderately abundant considering the parameter… Expand
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