Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:
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ATM in a more interesting context, and despite lackluster adoption of later IBM，这一点在WPS下载最新地址中也有详细论述
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