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The features, synthesis and utilization of the pentafluorosulfanyl groups have recently been reviewed (Savoie and Welch 2015).
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The method combines two techniques: (1) the Histogram Region of Interest Fixed by Thresholds (HRIFT) is designed to capture the color information of INIs for basic feature extraction; (2) an automated feature synthesis, based on the HRIFT features, is designed to identify OPMD by means of Genetic Programming and the Expectation Maximization algorithm (GP-EM) for classification improvement.
Proper designing of the applied fitness measure plays an essential role in the feature synthesis process.
Figure 2 An example of an ideal feature synthesis performed to 2D FVs representing three classes.
Designing additional fitness functions for particular problems is thus one of the main advantages of the proposed feature synthesis scheme.
The term "feature synthesis" was first used by Krawiec and Bhanu [17] in the context of object recognition.
Two separate audio feature extraction approaches are used to evaluate the performance of the feature synthesis with different types of features.
However, after performing the feature synthesis procedure with the found synthesis parameters, substantial DM improvements were obtained for all the FSs.
Figure 3 Two examples of feature synthesis (or transform) performed on 2D (upper case) and 1D (lower case) feature spaces using SVM.
In this article, we aim to overcome the mentioned problems by proposing an evolutionary feature synthesis (EFS) technique based on PSO.
The structure of the proposed feature synthesis technique is particularly designed for parallel computing, so that grid computingd was utilized in computing the results.
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