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Decision trees are constructed iteratively through a greedy and top-down procedure which maximizes the log-likelihood criterion [34, 35].
Similar to the classical hard decision tree building algorithm, the soft decision tree is built iteratively through a greedy and top-down procedure which maximizes the log-likelihood measure.
In this solution procedure, in order to avoid the computation of directivity values at different radiation angles during iterations to determine the beam pointing direction, the optimization model in Eq. (6) can be converted to a new procedure which maximizes the boresight directivity at boresight direction (0, 0).
The designed sequences are thus overstabilized, probably because of our optimization procedure, which maximizes stability.
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The decoding stage, i.e., determining the most probable state sequence given an observation, is performed with the help of Viterbi decoding procedure [31] which maximizes the a posteriori probability: {mathcal{S}}^{opt}=mathit{arg} {max}_{mathcal{S}}left( logPleft(mathcal{S}|{O}^{opt}right)right) (18).
The results suggest an appropriate balance has been reached which maximizes the use of kidney biopsies while minimizing the risk of this invasive procedure (word count: 202).
which maximizes the criterion (13).
which maximizes the resulting SNR.
This is reasonable, since our method, unlike Cox regression which maximizes a partial log likelihood, directly maximizes the penalized GAUCS.
More recent advances include OptForce, which maximizes product yield by knockout, inhibition, and activation targets, relative to a wild-type flux distribution [ 14], and EMILiO, which rapidly identifies the optimal set of modified reactions and their optimal fluxes using a successive linear programming procedure [ 11].
This paper develops theory for a class of subset selection procedures that identify product designs which maximize the worst-case performance over the environmental conditions for general combined array experiments.
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