In the objective function (7), the aim of the first term is to assess the fitness of labels which means that the predicted labels F should be close to the labels of the labeled data samples.
In this utility function, the first term denotes the receiving costs of BS m from all users in the same cell.
In the objective function of the problem in (12), the first term is a function of T ab and not related to T ba.
Specifically, Equation 34 is a nonconvex function of y and a, but the first term in Equation 34 is a function only of 1 wh 1 ′ y = ρ and μ D ̂ ′ a = ω and, thus, we rewrite Equation 33 in the same way as that in the previous subsection.
For sufficiently high values of k 2, the first term of the cost function vanishes, and hence, the cost function tends to be (k_{3} |s_{5}-hat {s_{5}}|).
where JCDL W,b) is an energy function, M is the number of training samples, the first term is an average sum-of-squares error term within all the samples, and the second term is the Frobenius norm that prevents over-fitting.
As the function (a i,z i )↦p y i:n |a i,z i ) is not a probability density function, approximating the second term of (13) using SMC samples is not straightforward.
If is an even function, then the third term in the formula of vanishes, while the sum of the first two items provides a singular Sturm-Liouville operator.
The proposed evaluation function in Eq. 4 consists of three terms: the first term on the right-hand side is the reproducibility between model parameters and observations, and the second term constrains the smoothness and discontinuity of model parameters (s).
