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Emphasis is also given to Fisher's linear discriminant method (LDA) for the two-class case.
Ross and Jain [20] combined face, fingerprint, and hand with sum rule, decision tree, and linear discriminant method.
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.
Fisher's linear discriminant method was used to compute the matrix directly.
Using Fisher's linear discriminant method, k was computed directly from known data, thereby simplifying the process.
The use of Fisher's linear discriminant method provides direction on how to incorporate different information into the prediction process.
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We suggest how to evaluate the rate of producing defects in Section 7, and some concluding remarks are presented in Section 8. Nowadays the SVM is one of the most standard tools as an efficient and effective non-linear discriminant method.
In Sections 2 and 3, we will introduce the above-mentioned non-linear discriminant methods and active learning methods, respectively.
Linear discriminant analysis method was used for qualitative analysis, correct classification percentages of straw, blends1, blends2 and coal were 89.87%, 79.66%, 94.92% and 100%, respectively.
Class prediction using k genes was carried out using a diagonal linear discriminant analysis method [ 25], which is a variant of the standard maximum-likelihood discrimination rule.
For Ward and Pim (1984) and Ruiz-Navarro et al. (1998), we used the linear discriminant function method to estimate relative risks from comparisons of means (Greenland 1987).
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