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In the proposed approach, the feature extraction process is automatically handled during training.
In our approach, the feature human model is constructed by unorganized cloud points obtained from 3D laser scanners.
Different from the regular SVR method, in order to meet the requirement of our approach, the feature extraction procedure is changed.
Comparing with the traditional approach, the feature points on the target which are considered as the primary features are preserved by the filter.
The motivation for pulsed laser micro polishing is to reduce the surface roughness of parts whose surface texture can approach the feature size.
Furthermore, approaches to transfer learning in the above three different settings can be classified into four contexts based on "What to transfer," such as the instance transfer approach, the feature representation transfer approach, the parameter transfer approach, and the relational knowledge transfer approach [51 54].
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In a recent work, Oliveira et al. (2014) evaluated their requirements engineering approach, the Feature-Driven Requirements Engineering approach (FeDRE), through a case study for developing an SPL of mobile applications for emergency notifications.
In the second feature extraction approach, the features are extracted directly from the short time frames.
In this approach, the features that are extracted from traffic measurements by using wavelet transformation are directly utilized in detecting changes in traffic flow.
We then presented a novel approach, the feature-based weighted Naïve Bayes model (FWM), which can address multicollinearity impacts among gene features and feature divergence between species.
In relevant approaches, the feature vector is extracted from the convolution of the original image with a 2D Gabor wavelet function at different orientations and wavelengths.
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