Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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5.3.1 The Algorithm<br />
The steps <strong>of</strong> our algorithm are as follows:<br />
1. For i= 1,2,..., L do<br />
1.1 Evaluate performance <strong>of</strong> classifier Di on validation response vector set<br />
RV<br />
V A<br />
i<br />
and denote it by P DIR(RV<br />
Di<br />
1.2 Use train response vector set RV<br />
T R<br />
i<br />
V A<br />
i<br />
based on LDA or nonparametric LDA.<br />
1.3 Evaluate the performance <strong>of</strong> classifier Di on RV<br />
).<br />
<strong>of</strong> classifier Di to construct eigenmodel<br />
V A<br />
i<br />
V A<br />
constructed in previous step. For m = 1, 2, ..., Mi select each Di(x mc ) from set RV<br />
V A<br />
i<br />
and do the following:<br />
in the eigenmodel<br />
and c = 1, 2, ..., C,<br />
(a) Project Di(x mc ) on LDA or nonparametric LDA-based eigenmodel.<br />
(b) Calculate the class scores by measuring the Euclidean distance from<br />
Di(x mc ) to the nearest sample from every class in the eigenmodel to<br />
generate ¯ Di(x mc ).<br />
(c) Use minimum membership rule on ¯ Di(x mc ) to produce crisp class<br />
label for x mc .<br />
(d) Cross check the generated class label with actual class label <strong>of</strong> x mc .<br />
Calculate the performance accuracy on RV<br />
If P EM<br />
Di<br />
V A (RVi ) > P DIR<br />
Di (RV<br />
continue for next classifier.<br />
V A<br />
i<br />
V A<br />
i<br />
and denote it by P EM<br />
Di<br />
V A (RVi ).<br />
) then continue, else go back to step 1.1 to<br />
1.4 For an unknown test sample x, select Di(x) from set RV<br />
¯Di(x) using the following steps.<br />
T S<br />
i<br />
and calculate<br />
(a) Project Di(x) on LDA or nonparametric LDA-based eigenmodel gen-<br />
erated in step 1.2.<br />
(b) Calculate the new class scores for x as ¯ Di(x) by measuring the Eu-<br />
clidean distance from Di(x) to the nearest response vector from every<br />
class in the eigenspace.<br />
The elements <strong>of</strong> RV<br />
T S<br />
i<br />
are now ¯ Di(x) instead <strong>of</strong> Di(x).<br />
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