we consider multicomponent map images with semantic layer separation and images that are divided into binary layers by color separation.
我们认为,多地图图像的语义层分离和图像分为二进制层分色。
To address the problem of multicomponent patterns that many researchers have forgotten to consider it in their classification systems, an adaptive boosting multi-label learning algorithm ( AdaBoost. MC) is developed based on decision trees, maximum a posteriori ( MAP) and the robust ranking principles.
为解决许多研究者在其分类系统中未予考虑的多成份模式问题,开发了一种基于决策树、最大后验(MAP)和鲁棒排序原则的自适应boosting多标记学习算法(AdaBoost.MC)。
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