When LED TVs hit the market just a few years ago a set like that could have easily cost twice as much if any manufacturer would have bothered to put such a high-end feature into a small set.
We may verify more accounts in the future, but because of the cost and time required, we're only testing this feature with a small set of folks for the time being.
However, because extensive spatiotemporal correlations in fMRI responses among neighboring voxels lead to high redundancy of features, only a small set of voxels with similar response profiles are selected.
To not overfit the features toward a small set of high frequency True interactions, we created a list of high frequency known gene gene interactions during tuning (see Section 2.5) and labeled these candidate relations as Unknown.
But in response to the problem of impersonators of brands and famous people, Twitter is giving us a sneak peak of an experimental feature they call "verified accounts". Twitter says it will start rolling out the beta of this feature this summer for a small set of public officials, public agencies, famous artists, athletes, and other celebs who run the risk of impersonation.
At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches.
Twitter says it will start rolling out the beta of this feature this summer for a small set of public officials, public agencies, famous artists, athletes, and other celebs who run the risk of impersonation.
Instead, Google is probably just testing some new features out to a small set of users, just as a lot of other sites do throughout the web.
Comparing with several state-of-the-art feature selection methods, our Linear Programming based Feature Selection (LPFS) method can select a small set of features by applying strong regularization while keeping high accuracy.
Another limitation to be considered is the lack of correlation between the neural and motivational measure, which may relate to methodologic differences in the two tasks, the imaging task featured single unique presentations of multiple images and the GFT featuring multiple presentations of a small set of images, each always in contrast with another image.
In order to achieve the best prediction results, it typically pays off to rather include many, potentially weak features than to manually pre-select a small set of discriminative features.
