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Owing to abundant spectrum resources, millimeter wave (mmwave) communication promises to provide Gbps data rates, which, however, may be restricted by large path-loss. Thus, antenna arrays are commonly used along with beam alignment (BA) as an important step to achieve the array gain. Efficient BA relies on the beam training codebook design. In this paper, we propose a new hierarchical codebook to...
Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by...
The Diversified Sensitivity-based Undersampling (DSUS) is an undersampling method to solve the imbalance pattern classification problems which overcomes the drawbacks of ignoring the distribution information of the training dataset in random-based undersampling methods. The DSUS trains multiple neural networks during the undersampling process. However, only the final one is used. In this work, we...
Machine learning with concept drifting attracts a lot of attention in recent years. However, there are only a few works on concept drift learning with imbalanced data. The Learn++.NSE, the Learn++.NIE, and the Learn++.CDS from the Learn++ family are three state-of-the-art learning algorithms designed to deal with machine learning with concept drifting. In this work, we firstly give a brief introduction...
The applications of remote sensing and GIS technologies in land cover classification contribute to the objective and accurate reflections of land use information. This paper reviews the development of land cover classification methods in China, including man-dominant land cover classification method and computer-dominant land cover classification method. And then taking a case study, this paper compares...
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