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I have the full code and the dataset. However i think i'm missing one part which author says in page 12 "
"Our classifiers treat every stroke individually. The estimation of the authenticity of the user is thus a highly volatile random variable. However, this
estimation can be rendered more robust by bundling several consecutive strokes and classifying them together. Instead of individually classifying all strokes and taking the majority
vote as the final decision, we combine the classifier output at an earlier stage. For SVM,
we average the continuous scores of projecting the individual test observations on the vector
orthogonal to the decision hyperplane. The final classification is then the thresholded average
score, depending on where to allocate the FRR against FAR trade-off"
I need to emulate figure 5 in my code. I am able to do it but i don 't how to implement
My code does give nsamples or strokes vs EER but it does not combine as the author says combine classifier output at an earlier stage and for svm average the continuous scores of projecting the individual test observations on the vector orthogonal to the decision plane. And the final classification is then the threshold average score depending on where to allocate the FRR against FAR.
1) you need to fix my code based on the author's claim above
2) Plot accordingly for each user like fig 5, provide minimum EER with respect to k or nsamples or stroke
3) [login to view URL] dataset
4) As per the author claims, you need to combine classifier output at an earlier stage
5) average the continuous scores of projecting the individual test observations on the vector orhtogonal to the decision plane.
6) Finally do the final classification based on the thresholded average score depending on where to allocate the FRR against FAR trade off
7) You just need to do SVM
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Bu iş için 5 freelancer ortalamada $186 teklif veriyor
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