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NetFlow based features (or flow based features) have been used to detect anomalies including botnets in a high speed, large volume data networks.
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General wavelet-based features have been proposed for image indexing and retrieval which can be computed during JPEG2000 compression (cf. [23]).
Eye component; Gabor jets-based features have been successfully used for analyzing the eye features providing classification rates of over 90% as well as fast convergence, surpassing other state-of-the-art methods [45, 55, 56].
The features, classification methods, and AUs analyzed in each component are detailed below: Eye component; Gabor jets-based features have been successfully used for analyzing the eye features providing classification rates of over 90% as well as fast convergence, surpassing other state-of-the-art methods [45, 55, 56].
In the context of speaker identification, auditory-based features have been extensively studied[27].
CS-based features have been used in numerous ways as a reliable tool to determine the modulation scheme of unknown signals [10, 14, 16].
In some cases, time- and frequency-based features have been combined [16, 30, 32] to further enhance the localization and classification performance.
Furthermore, spectral, cepstral, and auditory modulation-based features have been recently employed either in BOF approaches or as autonomous music representations in order to capture both the timbral and the temporal structure of music [19 22].
Sequence-based features have been employed for prioritizing disease genes by López-Bigas and Ouzounis (2004) and by Adie et al. (2005).
Ecological models based on such features have been developed using frequentist techniques (Omumbo et al., 2005), Bayesian approaches (Craig et al., 2007), and fuzzy logic (Snow et al., 1998).
Recently, two deep neural network methods based on atom-pair features have been reported [41, 42].
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