Kernel Negative ε Dragging Linear Regression for Pattern Classification
Input: Training samples matrix ; Label matrix ; dragging coefficient matrix ; test
sample ; parameter ;
Output: the slack variable class label matrix ; predicted class for test sample ;
Set threshold ; Set .
Given , calculate .
Utilize , then calculate .
Until the absolute value of the difference between objective functions of two consecutive
loops is smaller than threshold .
For test sample , calculate .
If , then is classified into the th class. is the th entry of .
Output: the transformation matrix ,.
We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at firstname.lastname@example.org to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.