The paper “The Importance of Convexity in Learning with Squared Loss” gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.

In this paper, the Weibull-Lomax distribution is considered for Bayesian analysis. The expressions Bayes estimators of parameter have been derived under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using quasi gamma priors.

Coprime arrays enable Direction-of-Arrival (DoA) estimation of an increased number sources. To that end, the receiver estimates autocorrelation matrix a larger virtual uniform linear array (coarray), by applying selection or averaging to physical array's estimates, followed spatial-smoothing. Both and have been designed under no optimality criterion attain arbitrary (suboptimal) Mean-Squared-Er...

in this research for detection and separation cultivation platforms and flowerpots from each other, localization the corner of platforms and position of the flowerpots to form of two-dimensional point and creation two-dimensional map of greenhouse, used three-dimensional coordinates of environment components. results obtained from this research, showed that the proposed algorithm can detect 100...