Therefore, the initialization procedure can be defined as a multi-objective optimization problem.
For any τ s ∈ T and for any t ∈ [ τ s, τ s + 1 ), our piecewise linearized procedure can be defined as: { x ( t ) = x ( τ s ) + χ ( t ), y i ( t ) = y i ( τ s ) + υ i ( t ), ∀ i ∈ N. Note that the planner has to observe the size, χ ( t ), but not each υ i ( t ), since the former determines the latter.
Further basic and clinical investigations are required until a procedure can be defined for the routine use of these cells in shoulder surgery.
Mathematically, this procedure can be defined as: where U ij is the Spearman rank correlation of genes i and j in data set U, V ij is the Spearman rank correlation of genes i and j in data set V, and the sums are over all distinct pairs of genes i and j.
The net impact of the uncertainties on the validation effort is assessed through the "noise level of the validation procedure", which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest.
The procedure of TC can be defined similar with other data classification tasks as the problem of approximating an unknown category assignment function F D × C → {0, 1}, where D is a set of documents and C is the set of predefined categories: F d, c = { 1, d ∈ D & d b e l o n g t o t h e c l a s s c 0, o t h e r w i s e (1).
The test procedure is modular and can be defined for each particular system, depending on the characteristics of the environment.
We illustrate that a wide range of clustering procedures, including generalized hierarchical methods, can be defined as parameters which are compositions of individual mappings for clustering patients and genes.
