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Neural Processing Letters

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06-07-2018

Robust Support Vector Regression in Primal with Asymmetric Huber Loss

Authors: S. Balasundaram, Yogendra Meena

Published in: Neural Processing Letters | Issue 3/2019

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Abstract

As real world data sets in general contain noise, construction of robust regression learning models to fit data with noise is an important and challenging research problem. It is all the more difficult to learn regression function with good generalization performance for input samples corrupted by asymmetric noise and outliers. In this work, we propose novel robust regularized support vector regression models with asymmetric Huber and ε-insensitive Huber loss functions leading to strongly convex minimization problems in simpler form whose solutions are obtained by simple functional iterative method. Numerical experiments performed on (1) synthetic data sets with different noise models and having outliers; (2) real world data sets, clearly show the effectiveness and applicability of the proposed support vector regression models with asymmetric Huber loss.

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Title: Robust Support Vector Regression in Primal with Asymmetric Huber Loss
Authors: S. Balasundaram
Yogendra Meena
Publication date: 06-07-2018
Publisher: Springer US
Published in: Neural Processing Letters / Issue 3/2019
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-018-9875-8

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