Classification of mixed variables

Accordingly, the models proposed in this paper, including the strategy that was adapted, were successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE).

23p spiritedaway36 28-11-2021 7 1   Download

Cropping pattern has undergone dramatic changes worldwide due to the eff ects of climate changes and human management activities. Cropping pattern is an major factor contributing to crop yield and food security at local, regional and national scales, and is a critical input data variable for many global climate, land surface, and crop models. Hence for creating annual cropping intensity maps at huge scales, MODIS images have difficulty with mixed land cover types within a pixel.

8p trinhthamhodang1216 19-11-2020 11 1   Download

A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis.

9p trinhthamhodang1 14-11-2019 11 0   Download