Your English writing platform
Discover LudwigSimilar(60)
A snapshot of the concept network is stored in a graph data structure.
For each of labeled concepts pair, we extract all the set of features described in the subsections titled Topological features and Semantically-enriched features from the snapshot of the concept network G t f.
4. The effect of using different snapshots of the concept network to generate training data.
For this purpose, we first take three consecutive snapshots of the concept network, each of which spans a 5-year period; then generate the first training data set from the first two snapshots and the second training data set from the last two snapshots.
Moreover, in order to automatically build the classification model for prediction, we take two snapshots of the concept networks corresponding to two consecutive time durations, such that a training data set can be formed based on a group of labeled concept pairs that are automatically extracted from the snapshots.
Since predictions are carried out based on a classification model that is built upon a training data set extracted from two consecutive snapshots of the concept network, the performance of link discovery can be evaluated by measures such as classification accuracy, recall, and precision as results of n-fold cross validation on the training data.
This is a total concept, a snapshot of his state of mind and an amazing art piece.
The proposed Monte-Carlo approach, which is based on the concept of snapshots, allows us to effectively calculate the performance of a design along its lifespan even up to the terminal stages.
Recall that the first training data set was extracted from concept network snapshots G t 1 and G t 2; whereas the second training data set was extracted from snapshots G t 2 and G t 3.
In Figure 10, we show the similar result of comparison for the second training data set that was extracted from concept network snapshots G t 2 and G t 3.
The purpose here is to provide a snapshot of the current concepts and future prospects of tolerogenic vaccination for Multiple Sclerosis, along with the central challenges to clinical application.
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