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The traditional BIEO label set is used in our post-challenge improved system.
The label set is obtained as the union of the relabeled label sets of the children, cf. constraint (17b).
The dummy label will somewhat change the training procedure: during triplet sampling in SGD, if (y_{ij}) is sampled, where j corresponds to the dummy label, then the irrelevant label set remains unchanged, otherwise, the new irrelevant label set is consisted of the union of original irrelevant labels and the dummy label.
A supertree of is a phylogenetic tree whose label set is ℒ (P ).
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The label set was divided along the midline to produce separate labels for each hemisphere, which were then used as seed regions for probabilistic tractography (Fig. 1 G, H).
The labeling of the storylines with as little overlap as possible in the positive and negative label sets is key to that.
Regarding the class-conditional probabilities, as the labeled set is only composed of man-made-only or natural-only images, we simply estimate them according to (11).
The consistent lower success rate of the training set comparing with the incomplete labeled set is due to the bias by which the training set is selected therapies that present a greater challenge than ususal and hence genotypic sequence is tested.
Several label sets are infeasible and thus are assigned a weight of ∞.
Because uncountable label sets are much less practical in this context, we develop in the present article a general procedure to trim an originally uncountable label set down to countable cardinality.
A k-labeling of a graph G with n vertices is an assignment of a set l v)⊂{1,2,…,n} to each vertex v of G, such that |l v)|=k and all label sets are distinct.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com