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In a number of classification problems, GFAM produces the optimal classifier.
Although these approaches have proved useful in a number of classification tasks (e.g. Aytuna et al., 2005; McDowall et al., 2009), here they added complexity but did not give a significant improvement in the performance of the methods developed here (data not shown).
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In this case, a number of classification algorithms, based on 2D histogram analysis, are obtained by multidimensional histogram projection which are focused on two color procedures.
In veterinary medicine, a number of classification proposals for canine epilepsy have been published over the years always reflecting in part the current ILAE proposals (e.g. [ 35– 35]).
In order to answer this question to the researcher, the following approach was made: After the user selects the potential features and the desired target class for a given dataset, a number of classification algorithms in numerous configurations are launched in parallel in the background.
Luckily, linear methods have proven a high performance for a number of classification tasks in the field of EEG-based BCI systems.
In an attempt to overcome participant heterogeneity, a number of classification systems have been proposed for identifying subgroups of people with LBD who might respond more predictably to specific treatment [ 12, 13].
A number of classification methods reported in the literature require selection of the relevant or informative predictor variables before modeling is actually performed.
A number of classifications are offered in the literature, one of the most popular being by Trampuz and Zimmerli (Zimmerli et al. 2004, Trampuz and Zimmerli 2008).
The goal is to address the training and detection runtime complexities found in an increasing number of classification domains.
The QUWI database which includes writer demographics information has been employed for gender classification in a number of recent studies and a highest classification rate of 69.25 % is realized [150].
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