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For example, German startup Tweek, which launched its iPad app at London Web Summit this week, is trying to solve the TV content selection problem by aggregating ìlikesî and analyzing sentiments of a social graph related to particular video content.
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For some examples of static summarization methods that we study in this paper, Cong et al. [7] considered video summarization as a dictionary selection problem, where keyframes that represent the whole video content form the dictionary.
This is a problem that largely stems from Watchable's still small content selection – YouTube is better for discovery because of its scale, and the ability to find videos around a given topic from its interface.
First, take the selection problem.
This is an "adverse selection" problem.
And they solve the adverse selection problem.
The developments leave Gatland with a potential selection problem.
The index selection problem is first formulated and four index selection algorithms are evaluated via simulation.
There is a big difference with the variable selection problem.
The IBS algorithm solves the implementation-bin selection problem.
Content selection is based on user preference.
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