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Clinical identification and rating of the cerebral microbleeds (CMBs) are important in vascular diseases and dementia diagnosis. However, manual labeling is time-consuming with low reproducibility. In this paper, we present an automatic method via deep learning based 3D feature representation, which solves this detection problem with three steps: candidates localization with high sensitivity, feature...
Accurate prediction for inbound tourism demand is important for development and implement of Chinese inbound tourism strategy. It has positive significance. BP neural network as a common traditional machine learning methods is widely used in travel demand forecasting model. However, BP neural network suffers from several drawbacks, such as over fitting, difficulties in setting parameters and local...
Managing the costs and risks of evolution is a challenging problem in the RE community. The challenge lies in the difficulty of analyzing and assessing the proneness to requirement changes across multiple versions, especially when the scale of requirements is large. In this paper, we define a series of metrics to characterize historic evolution information, and propose a novel method for predicting...
We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with genetic algorithm post optimization, for object detection that uses local shape-based feature. The feature is motivated by the scheme that use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to select a discriminative...
We propose a novel learning algorithm, called Bagging-Adaboost ensemble algorithm with floating search algorithm post optimization, for object detection that uses local shape-based feature. The feature use the chamfer distance as a shape comparison measure. It can be calculated very quickly using a look-up table. Random sampling boosting algorithm is used to form an object detector. Floating search...
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