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In these methods, the feature space is partitioned into subspaces and different probability density functions (pdf) are learned for the different streams.
In these methods, the feature space is partitioned into subspaces generated by the different streams, and different probability density functions (pdf) are learned for each subspace.
This method provides an advantage over other classification programs in ease of use and, unlike other methods, the feature vector is not constrained to a single protein family or superfamily [23].
For almost all methods the feature distances get significantly higher the farther away the patches are located from the CoD (i.e. the more distorted the patches are).
Similar(56)
To facilitate comparison with existing window-sliding methods, the feature-extraction module employs features that are often used by such methods: the integral features introduced in [ 78].
As described in Methods, the features for each classifier are ranked using an expanded variation of the basic SVM-RFE (Support Vector Machine – Recursive Feature Elimination) algorithm [ 39].
As a final step in the odometry method, the feature tracks need to be updated.
For feature generation method, the feature vector generated from combining pitch histogram and pitch-frequency scaled spectrum shows the best performance in the experiment.
By using Bicubic spline interpolation method, the feature detection time is about 1.5 times longer than that of our proposed downsampling method takes.
In case of proposed method, the feature dimension is only (2times 6=12), while in case of PAR4, PAR5 and PAR6, it is (4times 3times 3+4times 6=60), (5times 3times 3+5times 6=75) and (6times 3times 3+6times 6=90) respectively.
For each method, the feature number was chosen by minimizing the average error rates.
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