Sentence examples for of the data instances from inspiring English sources
An incremental clustering algorithm refers to an abstraction of the distribution of the data instances generated by the previous run of the algorithm and therefore is able to cope well with the ever-growing contemporary databases.
The procedure for augmenting the local schemas upon schema evolutions in the APIs of their data sources is automatically repeated to keep up-to-date metadata about the physical schema of the data instances ingested from each data source.
Each decision tree in a forest is constructed using a bootstrap sample from the data, and about one-third of the data instances are not used to grow the tree.
If all the components of the data instance vectors have the same physical units, it is then possible that the simple Euclidean distance metric is sufficient to successfully group similar data instances.
The second is that time alignment of multi-sourced data requires that data instances originated from the same node shall have the same interval, or the data instances of different nodes shall be generated at the same time [19].
The first is that data itself shall maintain the freshness which can be measured by the variation of time between the times of the current system and the data instance.
The optimal hyperplane maximizes the separation margin between the two classes of training data, and is defined by a fraction of the input data instances (the so-called support vectors) close to the hyperplane.
where Nα denotes the number of the training data instances belonging to class ω α and the kernel function is as defined below.
The optimal hyperplane maximizes the separation margin between the two classes of training data, and is defined by a fraction of the input data instances (called support vectors) close to the hyperplane.
A similar approach is used in this article to deal with the imbalanced data problem (only 7.25% of the total data instances are positive).
We denote this input feature matrix F, whose size is (M times N), where M is the number of data instances (the total number of presented stimuli) and N is the number of features (voxels).
