2023
DOI: 10.3390/math11030709
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Robust Online Support Vector Regression with Truncated ε-Insensitive Pinball Loss

Abstract: Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on a non-convex asymmetric loss function is developed to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated ε-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and o… Show more

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Cited by 4 publications
(1 citation statement)
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“…The paper authored by Shan et al [3] develops a robust online support vector regression algorithm based on a non-convex asymmetric loss function to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers.…”
mentioning
confidence: 99%
“…The paper authored by Shan et al [3] develops a robust online support vector regression algorithm based on a non-convex asymmetric loss function to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers.…”
mentioning
confidence: 99%