Your English writing platform
Discover LudwigSuggestions(2)
Exact(1)
We developed a probabilistic progressive approach with local thresholds to solve the problem.
Similar(59)
The reconstruction in [21] applies the weighted median operator and the iterative thresholding to solve the following L0-regularized Least Absolute Deviation regression problem.
Ollila et al. [50] derived an iterative hard thresholding algorithm coined Huber iterative hard thresholding (HIHT) to solve the problem (29).
3, we propose an iterative fraction thresholding algorithm to solve the regularization problem ((QP_{a}^{lambda})) for all (a>0).
We propose an iterative fraction thresholding algorithm to solve the regularization problem ((QP_{a}^{lambda})) for all (a>0).
Then, we propose an adaptive thresholding strategy to solve the inhomogeneity issue, based on the local information, and to accurately segment synaptic vesicles.
Pauker and Kassirer have recommended a decision threshold approach to solve conflicting issues such as treatment of SNPTB.
Blumensath and Davies [ 9] were the first to propose the iterative hard thresholding (IHT) to solve a type of ℓ0-regularized problems and showed that the IHT method converges to a local minimizer.
In addition, we propose projected alternative direction method (PADM) and alternative hard thresholding method (AHTM) to solve our proposed models.
We introduce a threshold cut technique to solve the IP problem efficiently, which is shown to be reasonable from the viewpoint of maximizing expected accuracy.
We propose a pre-signal threshold method in order to solve this issue.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com