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In general, the transform streamlines otherwise unwieldy computations by systematically partitioning the data.
Based on access pattern recognition, we build intelligence into the mechanism responsible for partitioning the data.
When multiple channels are available for transmission, the problem extends to that of partitioning the data across these channels.
Furthermore, the oblique tree construction aims at partitioning the data of the non terminal node into two subsets.
Segmentation of medical images is the task of partitioning the data into contiguous regions representing individual anatomical objects.
Essentially this involves partitioning the data in multiple ways each of which avoids training the classifier on the same data that is used to evaluate its performance.
Partitioning the data set and separately microaggregating each subset resulting from the partition is a way to make MDAV usable on large data sets.
It should be noted that this implementation can take up billions of molecules for screening, by using the technique of partitioning the data into batches.
Nonetheless, implementing the algorithm incurs other problems due to the complexity for partitioning the data domain especially when its dimension is equal to 4 or more.
A decision tree is built top-down from a root node and includes partitioning the data into subsets that contain instances with similar values (homogenous).
We have devised and assessed several ways of partitioning the data and combining the Machine Learning algorithms in order to achieve a good performance in the classification process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com