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Students were partitioned into three groups (A, B, or C).
For the shifting-based dynamic TDD scheme, the cells in a cluster are firstly partitioned into three groups according to different shifting directions: left-shifting group B L, right-shifting group B R, and non-shifting group B M. The cells in each group can only shift along single direction, i.e., left or right.
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.
To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD.
These experiments were partitioned into three groups.
The algorithm consists of the following steps: (iii) n subjects are randomly partitioned into three groups of comparable size.
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The experimental data from a soil database is partitioned into two groups for fitting and verification of the proposed method.
It is presumed that the set of factors can be partitioned into two groups such that the number of levels is constant within each group.
In MSFR and static FFR schemes, all the users and subcarriers are statically partitioned into two groups namely center and edge groups.
Here we use cross-validation technique on the training data to determine the parameters C 1 and C 2. The training data is partitioned into four groups.
For a given subject, this set can be partitioned into two groups: A = α 1, α 2, …, α n B = β 1, β 2, …, β n ( m − 1 ).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com