This paper addresses the same language representability problem for finite labeled Place/Transition Petri nets without capacity bounds on places.
Two toolsets allowing generation of finite labeled transition systems, for μCRL and PROMELA, respectively, were applied in this case study.
The idea is to be able to elicit a "truthful" response on sensitive feature(s) from the sampled respondents (of a finite labeled population of respondents), so that eventually the population mean of the sensitive feature can be unbiasedly estimated.
CBF maps were calculated from the perfusion-weighted images using a 2-compartment model [ 29] with a finite labeling duration [ 4, 30], as described previously [ 31].
For the formal description of these methods, we will use L = {(λj: j = 1...M} to denote the finite set of labels in a multi-label learning task and D = {(x i, Y i ), i = 1...N} to denote a set of multi-label training examples, where x i is the feature vector and Yi ⊆ L the set of labels of the i-th example.
Next we give a concrete example of the natural interval extension of the likelihood function over an interval of trees t in the star-tree space 0 T. The same ideas extend to any labeled box of trees k t when the number of branch lengths is greater than one and more generally to a finite union of labeled boxes with possibly distinct labels.
In this example, Bardi (2008): 229-230) analyses the infinitive construct as a prepositional phrase; furthermore, since Bandstra's account of Mood does not include the concept of a Predicator, the verb lālęḏęṯ does not have have a functional role in the Mood structure, although the verb wattosęp̄ is labeled Finite, as in the analysis above.
Let X be a finite set of labels.
Let X denote the domain of instances and let Y = {1,2,⋯,Q} be the finite set of labels.
Let {(x i, y i )}∈ X × Y, i=1,…, n be a set of data points where X∈ℝ D is an arbitrary feature space and Y a finite set of labels.
