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Boundaries between two classes are not well defined.
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Several researchers have pointed out that obtaining a classifier that discriminates between two classes is much easier than building one that simultaneously distinguishes among all classes.
The similarity between two classes is based on the two obtained lists of backoff paths.
The greater the F value is, the greater the distance between two classes is, namely, the greater the difference between two classes is, the better the classification results are.
The GB uses a shared covariance matrix, so the separating margin between two classes is also a linear hyperplane.
The Fisher linear discriminant approach is based on the search of a linear combination of the vector of features such that the dissimilarity between two classes is as large as possible [31].
The CDS averages i-vectors for a language, the model can roughly be regarded as a vector pointing from the origin to the mean of the Gaussian distribution for this language, so the separating margin between two classes is a linear hyperplane which is through the origin and equally divides the angle formed by the models of the two classes.
The overlap area between two classes is smaller for the F score than for the TFDF, so that using the features from the time-frequency region selected by F score has less misclassified data (error rate = 11.25%) than by TFDF (error rate = 13.13%) on the example.
DE genes between two classes were identified through an unpaired Student's t test.
This symmetry also means that the transition probability between two classes is the same as that between their matched counterparts.
A comparison of average values between two classes was performed by means of a t-test, and a comparison of rates between two classes was performed by means of a Chi-square test.
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Justyna Jupowicz-Kozak
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