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Our approach consists of top-down classifications and bottom-up optimizations.
Our contribution in this review paper is a consolidated top-down classification of spectrum occupancy prediction.
In this section, we discuss an unsupervised online learning method for fine-grained hand segmentation based on top-down classification and bottom-up optimization.
In this paper, we presented an unsupervised on-the-fly hand segmentation method which consists of top-down classification and bottom-up optimization.
One possible objection to our insistence on the necessity of taking contextual, qualitative, and developmental aspects of the "object" of the psycho-diagnostic assessment into consideration might be that such emphasis is today obviated by the "top-down" operational classifications, with weak or absent hierarchy rules.
Harvard's administration was strictly "top down".
Top, down, starting with hair.
We present a top-down cascaded classification method which segments hand hierarchically in levels of frame, superpixel, and pixel so as to reduce computational cost, in which the classifiers are trained on-the-fly so as to be robust to diverse users.
We also performed a top-down induction of a classification tree using both age and AMH as potential classifiers.
From the point view of top-down strategy, we divide the classification task into three parts: frame-level detection and superpixel-level and pixel-level classifications.
We concluded that these top-down and bottom-up spatiotemporal data classifications with cluster analysis represent useful ex-ante impact assessment tools and need to be supplemented by participatory assessments.
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