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By the P2P process, a minimal feature resolution of 229 ± 17 μm has been successfully achieved.
Secondly, three variable reduction strategies were tested, that include the use of i) support vector machines (SVMs) with principal components analysis for all the feature set space; ii) using genetic algorithms coupled with SVMs for feature selection; iii) use the ranked features list as produced by random forests for searching a minimal feature set to train a SVM model.
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The accuracy of this combined engine when a maximal feature set used is significantly higher than when a minimal set feature set is used.
2. Birmingham Object Recognition Battery (BORB) – Minimal Feature View Task provides a set of standardized procedures for assessing neuropsychological disorders of visual object recognition, based on tests developed from the cognitive neuropsychological literature.
A common feature of all approaches is the necessity to select a number of parameters, including the (expected or minimal) feature size, by the experimenter.
In a similar process a generative discriminative engine is developed for the case of the minimal feature set.
Remarkably, replacement of all missing additional features in the case of the ME engine when working with the minimal feature set did not result in a significant loss in performance (p = 0.3125 and p = 0.3120 with respect to AUC and accuracy, respectively).
In this paper, a supervised approach for word sense disambiguation using neural network with minimal feature sets was implemented.
(The minimal feature).
Unlike previously proposed methods, this work presents an algorithm that supports data mining and security defence for cloud DDoS attacks using minimal feature set.
This B-spline filter is effective in removing numerical artifacts and controlling minimal feature length in optimized structures when the B-spline basis functions span multiple analysis elements.
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