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This paper presents a new method which extracts and selects features from multi-channel EEG signals.
In this paper, we propose a transductive method which extracts paired preference information from the unlabeled test data.
In this paper, wavelet-based exponentially weighted moving average (EWMA) test statistic with adaptive thresholding method, which extracts several significant coefficients from original functional data in the wavelet domain and monitors out-of-control events, is proposed.
Moreover, we cite the Narrow band filter which is an alternative filterbank with a fixed narrow bandwidth and the comb filtering method which extracts a harmonic series using pitch information [23].
Their method, which extracts information on the relative durations of the glottal opening and closing phases from inverse filtered speech, performs very effective classification for a single-dimension feature, when evaluated on a six-emotion-acted speech database.
The PJD algorithm is a subspace method, which extracts the candidate eigenpair from a search space and the space updated by embedding the solution of the correction equation at the JD iteration.
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Unlike PseAAC-based fextractionraction methods which extract feature directly from protein sequences, kernel-based methods remain some kinds of prior information and therefore extract more comprehensive feature descriptors.
Feature-based methods, which extract and match the common structures (features) from two images, have been shown to be more suitable for this task.
Matrix factorization methods which extract latent features of users and items by decomposing the rating matrix have achieved success in rating prediction.
Both nematode extraction and DNA extraction from large (10 g) samples of soil gave reliable estimates of nematode numbers, but methods which extracted DNA from small (1 g or less) soil samples substantially underestimated numbers.
Unlike the conventional automatic modulation classification methods which extract modulation features based on a list of known patterns, this proposed method classifies modulations by the existence of basic modulation components including continuous frequency modulations, discrete frequency codes and discrete phase codes in an autonomous way.
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