Sentence examples for a negative instance from inspiring English sources

For instance, Crispin Wright and Stewart Shapiro say a competent speaker can faultlessly classify the borderline case as a positive instance while another competent speaker can faultlessly classify the case as a negative instance.

Thus, the instance (age= youth, student = no, credit_rating = fair) can be sorted down left the most branch of the tree and can be classified as a negative instance (i.e. tree predicts buys_computer = no).

For example, for the sentence in Figure 1, relation 〈Kip3, TaxonID: 7215〉 is a negative instance and the pair 〈Klp67A, TaxonID: 7215〉 is a positive one, where TaxonID: 7215 is the species ID for Drosophila.

In this case, the neighbourhood of a test instance located close to the boundary is more likely to be dominated by negative support vectors, which makes decision function more likely to classify an instance located close to the boundary as a negative instance [ 41].

It tries to derive a negative existential from a single negative instance, in that it allows us to infer that there is no way of grasping a given proposition such that the agent affirms it from the fact that there is one way (the way associated with the sentence she denies) under which she does not affirm it.

False positive is misclassifying an actual negative instance as a positive; false negative is misclassifying an actual positive instance as a negative; and true positive and true negative are the correct classifications.

We propose a mixed negative instance sampling strategy to learn the weights of different joint feature representations.

In Dover, a former Polish neighborhood that is now a haven for Mexican-American strivers, the authors find an entirely negative instance of "intergroup harmony," as white and Latino parents lobby to keep their children from being bused to schools in poor black neighborhoods.

The cellular function algorithm had a sensitivity of ∼11% and a selectivity of ∼3%, with a DR of 3.9, indicating that many motifs were recovered and there was a ∼4-fold preference for retaining a verified instance over a randomly selected negative instance.

These results represent the probability that the classifier will rank higher a randomly chosen positive instance than a randomly chosen negative instance [38].

The auc of a classifier is equivalent to the probability that the classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance.