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We propose a simple iterative method which starts with the previous resource fair case.
This paper uses the popular Non-hierarchical clustering method called K-mean method, which starts by random partitioning of items into K initial clusters and goes through the list of items for assigning an item to a cluster with a closest mean to an item. .
We used hierarchical agglomerative clustering method, which starts out by putting each observation into its own separate cluster.
(124) K-means is a clustering (partition) method, which starts with a predefined number (K) of points randomly selected from the data as seeds.
It seems tempting to speculate that the optimization method which starts with AV interval (diastolic optimization) might also contribute, since the 'optimum' AVI was practically identical in these two patient populations despite very different baseline PR interval.
We would like to design a variation of our method which starts with a sparsely sampled curve over a large range of values of α and iteratively narrows the range of interest and increases the sampling density as information about the probable location of s* is obtained.
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One of them is the gradient projection method, which, starting from a feasible point, moves in a direction which decreases the error criterion and satisfies the above constraints.
But I hope that Lee's working methods, which start with casting and with listening to how the performers react to a given idea, mean that her mind and body will be forced to keep changing, too.
These ideas suggest the convenience of a more systematic use of field homology methods, which start with appropriately identified developmental fields to expand knowledge on adult homology (sameness) and eventually also on accessory structural and functional similarities or differences.
In this paper, we describe an "in silico" method (PepDesign) which starts by analysing the native interaction of a peptide with a target molecule in order to define which points are important.
In the Methods section we devise an adiabatic elimination method by which, starting from equation (8), we obtain a closed-form solution for a linear FPE that describes the time evolution of the joint probability density function of slow variables only.
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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