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Data transformation is necessary for learning algorithms, since it prevents the algorithm from accentuating the variables with bigger data.
The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.
Significance of this overhead is, however, decreasing with the expected number of active transmission sub-frames L (i.e., with bigger data packets and/or lower data rates).
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Are there any other challenges associated with big data?
"Eight (No, Nine!) Problems with Big Data," says the New York Times.
For every success with big data there are many failures.
A premium account brings deeper integration with big data: Facebook, Google, the Department of Justice.
The problem with big data is that it's not always that easy to wade your way through it.
And business intelligence software makers, like Microstrategy, are integrating their offerings with big data tools.
So yes, there are cutting-edge innovators with big data, but not a lot, it seems.
If so, Watson will be following other computers designed to deal with "big data".
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