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In this paper we develop a new spectrum sharing scheme that uses compressive sensing to support the coexistence of the sporadic machine-to-machine (M2M) communications and the persistent conventional communications such as the 5G cellular transmissions within the same channel. The redundancy in the transmitted signals, such as training symbols, pilots, MAC overheads and correlated data, is exploited...
In this paper, we develop a novel scheme to reduce the amount of training data required for training deep neural networks (DNNs). We first apply a partial mutual information (PMI) technique to seek for the optimal DNN feature set. Then we use a correlation matching based active learning (CMAL) technique to select and label the most informative training data. We integrate these two techniques with...
In this paper, we propose a joint machine learning and human learning design approach to make the training data labeling task in linear regression problems more efficient and robust to noise, modeling mismatch, and human labeling errors. Considering a sequential active learning scheme which relies on human learning to enlarge training data set, we integrate it with sparse outlier detection algorithms...
A remote sensing image classification method is presented based on AdaBoost algorithm in this paper. To solve the resampling of patterns, a weighted version is provided. The detail of implementation about the boosting algorithm is presented as well as experiments of the application on k-means, which proves the effectiveness of the implementation proposed in this paper. Further more, classification...
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