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This meager result implies that applied features, without any further processing, should not be used as a standalone input into GMM classifier in a speaker discrimination task.
For example, content-based classification and knowledge mining are suitable venues for the proposed framework, as are those in which the quality and description power of the applied features play an essential role.
Table 7 Comparison between different works in terms of features, ANN model and the achieved SNR with the recognition accuracy Reference Application Applied features Type of ANN Recognition accuracy [22] AMC Instantaneous temporal feature-based FANN (5,19,8) and PANN (5,1800,8) Overall success rate at -5 db are 65.63% and 55.5% respectively.
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e, f Iris-codes of applied feature extractor for sample image 001-02 of the IITDv1 database.
To make the results comparable, we chose to have a feature dimension of 160 for all applied feature space transformation techniques.
To verify performance of the applied feature selection method, it is necessary to assess model predictive behavior without any feature selection.
By analyzing the discriminativity of chosen features the authors show that the applied feature vector can be reduced by 45% maintaining error rates [127].
According to irreversibility, i.e., the possibility of inverting applied transforms to obtain the original biometric template, applied feature transformations have to be analyzed in detail.
Next, we applied feature selection algorithms from machine learning [21] to pick feature combinations that performed best at different tasks.
We applied feature selection to identify the most informative features.
In a second step we applied feature selection and supervised machine learning techniques.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com