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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.
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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.
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.
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.
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.
Badlands are highly erodible landscapes with sparse vegetation and rapid runoff responses.
The plant populations invariably maintain very low population densities with phenotypes that are highly efficient at using sparse resources and phenotypes (e.g., saltbush) that can tolerate environmental conditions that are lethal to mesic species.
As we are looking for the fixed point solution of the operator Ω, instability and large variations of the sparse solution are highly undesirable.
In spite of the high statistical supports, we must keep in mind that the sister relationships obtained here are highly tentative because of the sparse taxon sampling (76 species from 53 families) compared to the tremendous taxonomic diversity of the Percomorpha (ca. 14,000 species in 251 families; calculated from Nelson [ 23]).
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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