The phrase "a unified function" is correct and usable in written English.
It can be used in contexts where you are discussing a function that operates cohesively or serves a single purpose within a system or framework.
Example: "The software is designed to provide a unified function that integrates all user inputs into a single output."
Alternatives: "a cohesive function" or "a consolidated function".
She should also press the case for reforming the Dayton arrangements to create a unified, functioning state.
Unlike the aforementioned methods, MNet jointly optimizes the multiple network integration and the network-based classifier for a set of function labels in a unified objective function.
These data seem also to imply that the LV and RV may be working as a 'functional syncytium' and that the two ventricles cannot be dissociated in an independent manner since the architecture of the distinct myocardial bands makes it mandatory for an integrated and unified function of both chambers [18] [19].
A unified objective function with variable design priorities represented by function weights of the two topologies is proposed and examples are illustrated.
Yu et al. [ 25] proposed a method, called ProMK, that combines the composite network optimization with respect to a group of functions and the network based classifier in a unified objective function.
Then, a unified objective function is proposed to minimize the summation of these local loss functions over all the data points, so that an optimal predicated label value can be assigned to each data point.
Then, the manifold regularization term is incorporated into a multi-view topic model, resulting in a unified objective function.
A unified fitting function is established for fitting a group of faulted bedding planes with the consideration of local discontinuities due to faults.
This paper investigates semi-supervised learning of mixture models using a unified objective function taking both labeled and unlabeled data into account.
Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets.
The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated.
