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In Section 2, the methodology for feature extraction and classification is discussed in detail.
Section Auditory processing-based features describes the methodology for feature extraction.
The proposed methodology for feature selection is then presented in detail.
On the other side, the current ways to model features are considered problematic since it lacks a principled and uniform methodology for feature representation.
In this paper we present a methodology for feature extraction in Brazilian Sign Language (BSL, or LIBRAS in Portuguese) that explores the phonological structure of the language and relies on RGB-D sensor for obtaining intensity, position and depth data.
Then the process of extracting units of information (e.g., decomposing "lays eggs on the beach" into "lays eggs on the beach" and "lays eggs") was completed, following the McRae et al. (2005) methodology for feature normalization.
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The main contribution of the research has been the development of an attributed mid-surface adjacency graph to represent the mid-surface topology and geometry, and a feature recognition methodology for moulding features.
This kind of retrospective virtual screening is performed by using encoded molecule data and a neuro-fuzzy methodology for classification, feature selection, and rule generation.
We propose a decision tree-based methodology for our feature space partitioning.
A robust methodology is presented for feature set engineering using an open database comprising 552 intrapartum recordings.
Traditional statistical methodology is used for feature extraction, and GMM modelling is used to model the targets under different weather backgrounds.
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approaches for feature
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methodology for development
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