博文
Multi-Output Least-Squares Support Vector Regression Machine
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@ARTICLE{XAQZ+13,
author = {Xu, Shuo and An, Xin and Qiao, Xiaodong and Zhu, Lijun and Li, Lin},
title = {Multi-Output Least-Squares Support Vector Regression Machines},
journal = {Pattern Recognition Letters},
year = {2013},
abstract = {Multi-output regression aims at learning a mapping from a multivariate
input feature space to a multivariate output space. Despite its potential
usefulness, the standard formulation of the least-squares support
vector regression machine (LS-SVR) cannot cope with the multi-output
case. The usual procedure is to train multiple independent LS-SVR,
thus disregarding the underlying (potentially nonlinear) cross relatedness
among different outputs. To address this problem, inspired by the
multi-task learning methods, this study proposes a novel approach,
Multi-output LS-SVR (MLS-SVR), in multi-output setting. Furthermore,
a more efficient training algorithm is also given. Finally, extensive
experimental results validate the effectiveness of the proposed approach.},
keywords = {Least-Squares Support Vector Regression Machine (LS-SVR) sep Multiple
Task Learning (MTL) sep Multi-output LS-SVR (MLS-SVR) sep Positive
Definite Matrix},
}
https://blog.sciencenet.cn/blog-611051-665466.html
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