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We have used cross-validation to select the regularization parameter λ associated to the Group Lasso and Trace Norm regularizers, as well as the parameters λ and K in the case of the Matrix Factorization approach (K∈ [ 1,2,3,…,10], λ∈ [ 10−3,10−2,10−1,…,103]): the rows of Y are randomly partitioned into three groups of approximately equal size.
In this approach, the dataset is randomly partitioned into seven groups, each containing 1/7 of the dataset.
The algorithm consists of the following steps: (iii) n subjects are randomly partitioned into three groups of comparable size.
Wells were randomly partitioned into two classes for training the models (70% or 197) and validating them (30% or 84).
Each assemblage was randomly partitioned into two and 4900 bootstrapped angles between them calculated.
This set was randomly partitioned into two subsets (HapMap.A: n = 40; HapMap.B: n = 100).
Genes that are not currently labeled as CM genes were randomly partitioned into two equally sized sets of 'unknown' genes.
All ECoG features were randomly partitioned into five subsamples.
The full data set was randomly partitioned into three disjoint subsets, each with approximately one-third of the records.
The experimental dataset was randomly partitioned into five different training (80% of the peptides) and test (20%) sets.
Students were partitioned into three groups (A, B, or C).
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CEO of Professional Science Editing for Scientists @ prosciediting.com