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Minimizing surprise about future outcomes generally implies the minimization of a relative entropy, or KL divergence, between likely and desired outcomes.
LP focuses on maximization or minimization of a linear function over a polyhedron [15].
Our approach is based on the minimization of a weighted version of the l2-norm criterion.
Back propagation is based on minimization of a suitable error or cost function.
This estimation problem involves the minimization of a highly nonconvex cost function.
Our method is based on minimization of a weighted l2-norm of the prediction error.
This initialization is the unconstrained minimization of a convex function and it is bound to converge.
The reconstruction is realized by the minimization of a cost function using the steepest descent method.
The proposed algorithm is based on the minimization of a cost function.
The first is based on minimization of a criterion under constraints.
This method is based on the minimization of a fitness function.
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