Sentence examples for relation dataset from inspiring English sources

A binary relation dataset typically falls into one of the two categories: (1) choice data, for which each data entry (whether "0" or a "1") reflects an active decision (either a positive or negative relation); and (2) association data, for which the "0"s indicate only an absence of a relation, and are usually much less informative than the "1"s.

> -wrap-foot> Next, we applied seven previously published methods for extracting protein protein interactions to our connectivity relation dataset.

Knowledge discovery on binary relation datasets can benefit from a visualization of both decision-makers and choices in a common embedding space, where (simultaneously) (1) "Similar" objects (whether decision-makers or choices) ought to be "nearby" in the visualization;   (2) Decision-makers should be positioned "close" to their preferred choices.

There are some relation phrase datasets, such as Patty [19] and ReVerb [13] that can be used for this purpose.

The positive (i.e., in the sense of conflict mitigating) impact of decentralization is maximized in countries with high GDP per capita.30 Cederman et al. (2015) and Tranchant (2016) use the Ethno-Power Relations (EPR) dataset on all 800 politically relevant ethnic groups worldwide and find that territorial autonomy and fiscal decentralization, respectively, tend to reduce ethnic civil wars.

In addition to many known protein relations, our dataset allows for the inference of previously unknown functional links.

We can use machines to identify more complex signals and relations in datasets far bigger than any human could analyse.

We tested our models on the i2b2/VA relation classification challenge dataset.

The only difference between the two methods in relation to our dataset concerned slightly different bootstrap values.

Also we assessed the transcriptional concordance of our candidate genes in relation to the dataset by Lien et al.'s [ 30].

For example, maximal itemset mining leads to a drastically reduced number of patterns but also results in the loss of information on the relative importance of the itemset subsets in relation to the dataset.