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Exact(4)

In the KNN method, the features are first compared in order to determine the similarity of between a new case and previous cases.

In the first method, the features of the code error-signature are modeled to differentiate between the fault types and fault locations.

Using this piecewise method, the features that are being extracted are statistics of each partition of the time-series.

In our method, the features of the graph kernel-based method are extracted by the Stanford Parser [ 17].

Similar(56)

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.

With this method, the feature selection procedure is inbuilt to a classifier.

Due to the limitation of the sequence alignment method, the feature selection method was introduced in [ 9] to predict the remaining sequences.

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