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The simple estimation method works relatively well for small prediction windows.
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Besides, if no label proportions are provided (i.e., p k =1, k=1,…,K as defined in Naive GL), our method can still work relatively well, outperforming GL+SVM about 1% in classification accuracy (see the case of σ= max in Fig. 7).
The sampling-the-mean method works especially well for relatively small systems, systems with a density of energy states that contains sharp spikes or oscillations, or systems with little a priori knowledge of the density of states.
Note, however, that this method works best for those whose teeth misalignment is relatively minor.
First, we observe that the Purvine method works well in many cases, and is consistent; a shortcoming is relatively little ability to adjust to incoming data.
His method works".
This method works well.
This method works best overnight.
This method works in mathematics.
Either method works as well.
These methods have been shown to work relatively well for two state systems, i.e. for sorting out mixed ribosome populations [48] and mixed chaperone populations [51].
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