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.
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.
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.
However, although var2csa repositioned and was highly transcribed, the sparse presence of translational products and absence of surface expression imply an additional layer of control, which remains to be further investigated.
His sparse output was highly praised from the moment his first collection, 17 Poems, appeared in 1954 and he was acknowledged as Sweden's greatest living poet long before he won the Nobel.
The population is extremely sparse.
However, their cartoon component still contains some texture and their texture is not highly sparse, i.e., the non-zero coefficients are only due to texture component, see Fig. 4 a and d, respectively.
