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The essence of CS is to exploit prior knowledge of sparsity of the objective signal.
Using this techniques, rare cases can also effectively exploit prior knowledge from few samples in training set.
These algorithms exploit prior knowledge about the temporal channel statistics through the use of statistical channel models, which allows one to improve the multipath performance of the receiver.
Here we design a new nonparallel support vector machine (U-NSVM) that can exploit prior knowledge embedded in the universum to construct a more robust classifier for training.
SSA has also been considered when missing measurements are present [35, 36]; however, the proposed methods differ from our work in that we exploit prior knowledge in the form of a dictionary.
Each model of SNPSVM considers not only the compactness in both classes by the structural information but also the separability between classes, thus it can fully exploit prior knowledge to directly improve the algorithm׳s generalization capacity.
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The strong requirements of the ADCs can be reduced by exploiting prior knowledge on the signal model.
Recently, some works have investigated the way of exploiting prior knowledge to improve the performance of compressive sensing [7 9].
The key novelty is that GL-PIC employs more reliable estimates by exploiting prior knowledge of the constellation points.
Intuitively, this concept not only exploits the observations to estimate the hidden channel parameters but also exploits prior knowledge about the statistical dependencies between successive sets of channel parameters.
A different approach is taken in discrete tomography, in which objects that consist of a limited number of materials can be accurately reconstructed by exploiting prior knowledge about the expected materials [13].
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