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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 letter, we develop an active learning algorithm to optimize the selection of training data for robust linear regression. This algorithm selects training data based on the principle of correlation matching between the training dataset and the overall data pool. Considering the inevitable and potentially heavy human labeling errors, we model the probability of labeling errors based on the item...
Considering the fact that the wireless sensor networks (WSNs) need to maintain a long lifetime, there is a great demand to decrease energy dissipation of the sensor. Data compression is an efficient method to solve the problem. This paper proposes a practical and efficient data compression algorithm with high compression and noise-resisted features, in which the quasi-cyclic low-density parity-check...
In contrast to current systems where the spectrum allocation is static, future cognitive radio devices will be able to seek and use in a dynamic way the frequencies for network access; this will be done by autonomous detection of vacant bands in the radio spectrum. Many of the cognitive systems use highly complex algorithms for enabling dynamic spectrum access. With a variety of hardware systems available...
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