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As a metric for the hierarchical clustering we used the correlation between the parameter vectors (normalized inner product), and used average linkage as the method for hierarchical agglomeration of clusters.
However, we use the strategy of DE, letting the perturbation vector be the difference between the parameter vectors of two additional parents, j′ and j′′, chosen at random from the t-th generation of parents.
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Often, the prediction is a function of a dot product between the parameter vector and the feature vector.
The second term of Eq. (10) is a regularization term that correlates the parameter vector between slices.
the parameter vector.
Resolving between these subsystems is accomplished by adding vectors m j, which indicate the average spatial position of each LD, to the parameter vector θ j.
Let θ=⊤ be the parameter vector.
The parameter vector x is optimized iteratively.
We seek to estimate the parameter vector of the GMM,.
We next randomly permuted the n values for each parameter to get the parameter vector.
The method is based on the use of the plant simulator and on the generation of artificial data from which the relationship between the unknown parameter vector and available measurements is estimated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com