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Learning this feature mapping is based on two assumptions: (1) the feature mapping G is highly sparse and (2) the transformation is class-invariant meaning all classes share the same mapping.
As our geodesic metric function outputs only the positive value, we then use Dijkstra algorithm (Dijkstra, 1959) to find the shortest path from the seed-location P s to every other vertex in G. Since G contains only edges of adjacent voxel-vertices, it is highly sparse.
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To construct our App-DDoS defense model, we first projected the proposed attribute matrix into a subspace spanned by the initial principal components,, because it was highly sparse matrix.
These classification results lend support to the simple idea on which TNB was anchored i.e., in order to avoid ending up with a highly biased Naïve Bayes classifier, when the value of L is large and the training pattern vectors are highly sparse (in the sense described before), penalise appropriately the contributions from absence of features to the classifier.
Now, based on the data sets utilised, we may conclude: combining feature selection with apt penalization of absence of features can improve the classification performance of the Bernoulli Naive Bayes algorithm, in particular when the value of (L_{s}) is large and the training pattern vectors are highly sparse (in the sense described in "Background").
In fact, it is common that the distance distributions of the bacteria genomes are highly sparse for distances above 50.
The corresponding T-1 would be highly sparse, as it is for A. This also means that if numerous parents are selected, then this algorithm is expected to run very fast.
The population is extremely sparse.
On the other hand, spatially white noise is highly amplified by the sparse BM whereas the eigenspace BM slightly suppresses the noise.
The presence of outliers in CS measurements leads to the study of robust estimators since the recovered sparse signal is highly affected by the presence of the large errors in the data.
The loess is highly subject to erosion because of sparse vegetation, heavy precipitation in summer, and gullying.
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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