Sentence examples for processes for feature from inspiring English sources

Other work in this area has involved iterative processes for feature reduction, however these have been based on different methods [ 23].

To address this issue and achieve even better system performance in terms of processing time and recognition accuracy, a natural starting point is to perform an in-depth examination of the system processes for feature selection (FS) and feature reduction (FR).

This procedure first does all the process for feature detection and matching and iterates itself to search for the points found in the previous frame.

Additionally, the edge detection method stands out as an auxiliary process for feature extraction [65], segmentation and recognition [28], and image analysis [9].

To overcome this shortcoming, we introduce in this paper a theoretic framework based on rough set theory, which is called positive approximation and can be used to accelerate a heuristic process for feature selection from incomplete data.

The signal in each sub-band was down-sampled to 100 Hz and processed for feature extraction.

Then each time-series of spiking activity was sub-band filtered and processed for feature extraction and classification as described above.

One example of such a process for feature reduction is Support Vector Machines Recursive Feature Elimination (SVM RFE), which is a form of backward feature elimination.

The association mechanism between MAT and NC machining is established to reveal the association between geometry and NC process for features.

The selection process involves low-level, bottom-up processes for sensory feature extraction, and cognitive top-down processes for the generation of active motor commands that control the positioning of the sensors towards the most informative regions.

This includes discrete processes for extracting features to normalize and recognize images.