The phrase "a vector of outputs" is correct and usable in written English.
It can be used in contexts related to mathematics, computer science, or engineering, particularly when discussing data structures or functions that produce multiple results.
Example: "The algorithm generates a vector of outputs that can be analyzed for performance metrics."
Alternatives: "a set of outputs" or "an array of outputs".
That is for each multivariate input, we compute a vector of outputs (f^*) of the trained GPR.
In the following subsections, it is assumed that there are n DMUs and for each DMU _j) ((j = 1,ldots,n)) a vector of inputs ((X_j)) is considered to produce a vector of outputs ((Y_j)), where (X_j = (x_{1j},x_{2j}, ldots,x_{mj})) and (Y_j = (y_{1j},y_{2j}, ldots,y_{sj})).
That is for each multivariate input, we compute a vector of outputs (f^*) of the trained GPR. (ii) In the second step, we estimate a regularized linear regression (Haykin 2008) from a training set that consists of tuples, (D^f=left{ {( {f_1,y_1 }),( {f_2,y_2 }),ldots,( {f_n,y_n })} right} ).
The aim is to minimize the following objective function: underset{Delta P}{arg min}left({{leftVert P+Delta PrightVert}^2}_{{mathrm{A}}^{-1}}+{{leftVert JDelta P-vrightVert}^2}_Wright)kern0.36em (5)where P is a vector of output prediction parameter obtained from PDM and LNF.
The herein presented bootstrap approach is applicable to any predictor-based model, i. e. for any model that can return a vector of predicted outputs y ^, given a vector of parameters θ.
It should be noted that option A only requires an IO table in current prices and a vector of gross output deflators.
It is assumed in DEA that there are (n) DMUs and for each DMU j ((j = 1, ldots,n)) is considered a column vector of inputs ((X_{j} )) to produce a column vector of outputs ((Y_{j} )), where (X_{j} = (x_{1j},x_{2j}, ldots,x_{mj} )^{T}) and (Y_{j} = (y_{1j},y_{2j}, ldots,y_{sj} )^{T}).
Let y be a vector of observed or output variables.
For any arbitrary positive integer l and an l × l invertible matrix F with entries in {0,1}, consider a process that a random l-vector ( {mathbf{U}}_1^l ) with a uniform distribution over {0,1} l firstly multiply the matrix F over GF(2) and then passes through the channel ( W mathcal{X}to mathcal{Y} ) to generate a random vector of output.
Multi-valued consensus functions defined from a vector of inputs (and possibly the previous output) to a single output are investigated.
Each summation neuron sums the contributions for each class of the input to produce at the net output a vector of probabilities.
