Sentence examples for small data chunks from inspiring English sources

The fundamental principle of dynamic ensemble learning is to dividing large data-stream into small data chunks.

To study how to divide a dataset into small data chunks such that even during parallel sanitization, the minimum length/DoC transaction can be modified first.

The fundamental principle of dynamic ensemble learning is to dividing large data-stream into small data chunks and training classifiers on each data chunk independently.

While ensemble learning algorithms use the divide-and-conquer method to cutting up large data into small data chunks and training classifiers on each data chunk independently, then a heuristic algorithm is used to ensemble these classifiers together.

We can point out that in a smooth concept drift we tend to select a relatively large data chunk and small size of classifiers while in the case of abrupt concept drift a small data chunk is better.

Firstly, it can set the size of data chunk to fit different level of concept drift: small data chunk for sudden concept drift and large data chunk for smooth concept drift.

We can see that in a case of little or no concept drift a large data chunk is better and in a case of concept drift small data chunk is better.

In general, in order to ease the parallel execution of data-intensive applications input data is divided into smaller data chunks that can be processed separately.

Those smaller data chunks derived from the original datasets eventually exhibited an extent of 13 latitude raster cells by 34 longitude raster cells by 14,975 days, respectively.

In order to make a visual representation of the concept drift, we divided these data-streams into small data-chunks.

In ensemble learning large data-stream is divided into small data-chunks, and we train classifiers on each chunk independently.