@inproceedings{c262be4e3cc54caaace1132778ed507c,
title = "ε-Distance Weighted Support Vector Regression",
abstract = "We propose a novel support vector regression approach called ε-Distance Weighted Support Vector Regression (ε-DWSVR). ε-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the ε-tube is different from that of the boundary data containing support vectors. The proposed ε-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle this issue. To solve the new optimization problem arising from ε-DWSVR, we adoptdual coordinate descent (DCD) with kernel functions for medium-scale problems and also employ averaged stochastic gradient descent (ASGD) to make ε-DWSVR scalable to larger problems. We report promising results obtained by ε-DWSVR in comparison with five popular regression methods on sixteen UCI benchmark datasets.",
keywords = "regression analysis, Support Vector Regression, Distance Weighted Support Vector Regression, Dual Coordinate Descent, Averaged Stochastic Gradient Descent",
author = "Ge Ou and Yan Wang and Lan Huang and Wei Pang and Coghill, {George MacLeod}",
note = "We gratefully thank Dr Teng Zhang and Prof Zhi-Hua Zhou for providing the source code of “LDM”, and their kind technical assistance. We also thank Prof Chih-Jen Lins team for providing the LIBSVM and LIBLINEAR packages and their support. This work is supported by the National Natural Science Foundation of China (Grant Nos.61472159, 61572227) and Development Project of Jilin Province of China (Grant Nos. 20140101180JC, 20160204022GX, 20180414012G H). This work is also partially supported by the 2015 Scottish Crucible Award funded by the Royal Society of Edinburgh and the 2016 PECE bursary provided by the Scottish Informatics & Computer Science Alliance (SICSA).; 22nd Pacific-Asia Conference 2018, PAKDD 2018 ; Conference date: 03-06-2018 Through 06-06-2018",
year = "2018",
doi = "10.1007/978-3-319-93034-3_17",
language = "English",
isbn = "9783319930336",
series = " Lecture Notes in Artificial Intelligence ",
publisher = "Springer",
pages = "209--220",
editor = "{ Phung}, Dinh and Tseng, {Vincent S. } and Webb, {Geoffrey I.} and Ho, {Bao } and Ganji, {Mohadeseh } and Rashidi, {Lida }",
booktitle = "PAKDD 2018: Advances in Knowledge Discovery and Data Mining",
}