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In previous research, the discriminant method was used to combine these test results.
To overcome this difficulty, we have proposed the LSVM which uses a linear discriminant method by consciously forgetting the nonlinearity of the discriminant function at the sampling stage in active learning.
In this study, the Fisher's discriminant method was used.
Due to these limitations, we define a scoring function in terms of classification accuracy provided by multivariate discriminant methods, such as SVMs, BBNs, neural networks, or decision trees.
The discriminative common vectors (DCV) algorithm is a recently addressed discriminant method, which shows better face recognition effects than some commonly used linear discriminant algorithms.
Emphasis is also given to Fisher's linear discriminant method (LDA) for the two-class case.
Before using the discriminant function method, we need to specify the type of discriminant function.
The discriminant function method is to design a discriminant function f c (x) for each class c, where x is the feature vector of a test pattern.
Fisher's linear discriminant method was used to compute the matrix directly.
These were analyzed statistically by the discriminant analysis and the prediction with the rules were evaluated by the elimination method.
Group memberships were analyzed and confirmed by the discriminant analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com