Image processing was performed using supervised classification, with GRASS GIS software.
A clustering analysis has been applied to a data set and the classification has been performed using supervised algorithms.
The classification of this local context is performed using supervised machine learning based on Support Vector Machines (SVMs), given their demonstrated effectiveness in such applications [15].
On the detected faces and computer generated sketches, feature extraction is performed using supervised auto-encoder to build deep architecture and matching is performed between computer generated sketches and face photos using Parallel Convolutional Neural Network (PCNN).
The following 3 general categories of tasks can be performed using image features: statistical comparisons, supervised learning, and unsupervised learning.
The land use analysis was performed using a supervised classification technique based on the Gaussian maximum likelihood (GML) algorithm with training data (collected from the field using GPS).
For this purpose, diethyleneglycol coating of gadolinium oxide (Gd2O3-DEG) was performed using new supervised polyol route, and small particulate gadolinium oxide (SPGO) PEGylation was obtained with methoxy-polyethylene-glycol-silane (550 and 2,000 Da) coatings as SPGO-mPEG-silane550 and 2,000, respectively.
Supervised analysis was performed using the non-parametric Mann-Whitney two-sample test (CGH test [ 19]).
The supervised analyses were performed using SAM software version 3.0 [ 29, 30].
Supervised analysis was performed using the Significance Analysis of Microarrays software using 2-class unpaired test and 100 permutations.
