The profile consists of features that are constructed using the history of user actions on her mobile device over time.
For a given data (X in {R}^{n times d}), each (x_i, i = 1 ldots n), corresponds to a sample or data point, which can be represented by a profile of d features, i.e., (x_i = (x_{i1}, ldots, x_{id})).
The effectiveness of the proposed methods was demonstrated and analyzed on the F1 measure, the profile of selected features, and dimension reduction rate for the IAC tasks.
Thus, the morphological profile of linked features differed significantly between FGCB and FGCB-HI, indicating distinct entities, with FGCB almost exclusively found in the unexplained group.
The intra-linguistic differences that were found in the profile of emotion features suggest that weightings based on this profile can be used in robotic modelling to create emotion-sensitive socially interacting robots.
Based on the profile of genomic features bound by Hmt1, we found that Hmt1-binding sites are located at a number of ORFs as well as noncoding RNA (ncRNA) genes such as tRNA and snoRNA genes.
Randomly selected features from the input data are placed in the grid location, or node, which best matches the intensity profile of that feature.
More importantly, the profile of this feature is universal, i.e., there is no clear change from source to source.
The channels selected are those that show similar behavior to the one expected in the spatiotemporal profile of the feature characterizing the pre-ictal state of the patient.
