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Hence, we can generate various feature vectors for images and use them to train a neural network.
To realize this, various feature vectors (see, e.g., [ 26, 44, 54– 64]) were proposed to express proteins or peptides by extracting their different features into the pseudo amino acid composition [ 53, 65] or Chou's PseAAC [ 66– 68] or general form of PseAAC [ 24, 69].
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In numerical weather prediction, various observed inputs, i.e., various input feature vectors, are obtained.
Figure 11 illustrates the reconstruction of the face images using the LP method for various PCA feature vector dimensions and biohash bit lengths.
The users could calculate various kinds of PseNAC feature vectors for given DNA sequences or FASTA files by selecting different methods and parameters.
Herein, BioDNA allows users to calculate various kinds of autocorrelation feature vectors for given DNA sequences or FASTA files by selecting different methods and parameters.
The proposed approach is tested on various combinations of dataset and different combinations of audio feature vectors with various Indian male and female PS's songs.
Feature vectors from various real and artificial fingers are used as the training data for the selected machine-learning method.
Lastly, SUMOhydro [ 23] is mainly based on input feature vectors based on various representations, including, but not limited to, traditional binary encoding, the composition of k-spaced amino acid pairs (CKSAAP), position specific scoring matrix, six-letter, nine-letter, and 'hydrobinary' encoding.
In contrast to unsupervised methods, e.g. correlation methods, the supervised approach does not directly operate on pairs of expression profiles but on feature vectors that can be constructed in various ways.
Table 1A depicts the results obtained when the feature vectors are extracted from the hyper-spectral data that has been subjected to various decorrelation schemes.
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