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This required the use of exemplars (representatives) from each of the major groups.
Although semantic links can be defined in other ways (e.g., superordinate categories and their members), the use of exemplars is advantageous for the study of semantic effects, because exemplars share lexical representations (names), functional properties, and features.
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The critical difference between Damian's original study and recent studies that report priming with novel primes appears to hinge on the use of typical exemplars from well defined semantic categories.
To bridge this knowledge gap, the current study focuses on investigating the factors influencing patients' acceptance or ongoing use or dis-continuation of use of an exemplar consumer e-health service – a patient e-appointment scheduling service – through a longitudinal case study in a primary health care clinic.
A potentially important side effect of using exemplars consisting of 30 subsequent frames in [20],[24] was that the conversion of state activations to state posterior probabilities involved averaging over 30 frame positions.
On the other hand, some ATR approaches [8, 1, 9] depend on the use of multi-view exemplar templates to train a classifier.
A nonredundant list of mouse genes was annotated from the set of UCSC Known Genes as in Calabrese et al. (2012) via the use of the longest exemplar per gene to count allele-specific expression.
Therefore, we will compare recognition accuracies obtained when estimating state likelihoods using a single set of exemplars represented by 135-D feature vectors and the fusion of the state likelihoods estimated from the 135-D system and nine sets of exemplars (one for each modulation band) represented as 15-D feature vectors (for the 15 gammatone filters).
The order of exemplars used, and the side-of-stimulus presentation was determined by an a priori pseudorandom list.
In the experimental design, we used a small set of exemplars, which were presented repeatedly throughout a session.
Second, for these modalities, the classifier failed to predict the semantic category of a previously unseen item, suggesting that the classifier could not distinguish the semantic classes, but only the exemplars, possibly through the use of perceptual differences between the exemplars.
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CEO of Professional Science Editing for Scientists @ prosciediting.com