Loading...
Iterative learning in support vector regression with heterogeneous variances
Wu, Jinran Wang, You-Gan
Wu, Jinran
Wang, You-Gan
Author
Abstract
The presence of heterogeneous variances is the norm in practice, which makes machine learning predictions less reliable when noise variances are implicitly assumed to be equal. To this end, we extend support vector regression by allowing a range of variance functions in the model training. Specifically, we model the variance as a function of the mean and other variables as traditionally used in statistical modeling. This leads to iterative learning between support vector regression training and heterogeneous variance modeling. Extensive simulations are implemented to validate the effectiveness of the proposed framework in both linear and nonlinear regressions. Finally, two real data sets are used to demonstrate the superiority of the proposed algorithm in the presence of heterogeneity.
Keywords
noise modelling, heterogeneity, iterative learning, parameter estimation, prediction
Date
2023
Type
Journal article
Journal
IEEE Transactions on Emerging Topics in Computational Intelligence
Book
Volume
7
Issue
2
Page Range
513-522
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled