Sample was further differentiated according to Leonhard's classification featuring disease entities with specific combination of bipolar and psychotic syndromes.
Abundance matrices of phylogenetic abundance per sample were calculated at different taxonomic levels from OTU and respective taxonomic classifications features.
From OTU abundance and their respective taxonomic classifications, feature abundance matrices were calculated at different taxonomic levels, representing OTU and taxa abundance per sample.
By these processes, it can obtain nonlinear classification features efficiently.
Than obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet coefficients.
It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features.
The most important factor affecting the performance of a BCI (Brain Computer Interface) systems, is classification feature set.
The classification feature enables MPLS (Multi-protocol Label Switch) to support differentiated types of services (DiffServ) with needed QoS.
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.
Because most good text classification features are positively correlated (i.e., they occur more often in than in ), one may want to explicitly rule out the selection of negative indicators.
We also use words and POSs as classification features.
