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In [22], the problem of linear transceiver design for downlink network MIMO with per-base-station power constraints is studied, with various criteria based on minimizing mean squared error.
The data is divided into fifths, training initially occurs on four fifths of the data where a best model is chosen based on minimizing mean square error.
Salmonid spawnings based on minimizing mean kinship are now being carried out in a number of captive breeding programs (e.g., Flagg et al. 2004a; Hansen and Jensen 2005; O'Reilly and Doyle 2007; Kozfkay et al. 2008).
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Among the existing algorithms for ANC of impulsive noise, one is based on minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm.
The optimal estimate is derived by the Kalman filter based on minimizing the mean square error [22].
The final model for all traits based on minimizing the mean squared error was constructed from 1200 trees at an interaction depth of 5 and a shrinkage parameter of 0.0075.
While this estimation may be sufficient for most purposes, Scharr [38] proposed a new operator that uses an optimized filter for convolution based on minimizing the weighted mean-squared angular error in the Fourier domain.
Hence, optimal design for training symbols based on minimizing Cramer Rao lower bound [7], minimizing mean square error (MSE) of estimation [8 10], and maximizing lower bound capacity [11] has been considered in literature.
The proposed family is based on minimizing the general least mean cost function via stochastic gradient optimization.
The K-means clustering has an animal's phenotype values added to a cluster based on minimizing differences from the means of the K clusters.
The optimum number of neurons and transfer functions for the hidden layer was determined based on minimizing the value of the mean squared error (MSE) of the training and prediction datasets.
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