Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data
碩士 === 國立高雄大學 === 統計學研究所 === 106 === For the past decade, generalized support vector regression methods have been applied to model the interval-valued data. Based on the idea of support vector regression (SVR) and twin support vector regression (TSVR), Peng et al. (2015) proposed a novel interval tw...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | en_US |
Published: |
2018
|
Online Access: | http://ndltd.ncl.edu.tw/handle/4n36m4 |
id |
ndltd-TW-106NUK00337004 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-106NUK003370042019-05-16T00:37:23Z http://ndltd.ncl.edu.tw/handle/4n36m4 Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data 基於粒子群優化算法的延伸雙支持向量迴歸應用於區間數據資料 CHEN, CHIA-WEI 陳嘉緯 碩士 國立高雄大學 統計學研究所 106 For the past decade, generalized support vector regression methods have been applied to model the interval-valued data. Based on the idea of support vector regression (SVR) and twin support vector regression (TSVR), Peng et al. (2015) proposed a novel interval twin support vector regression (ITSVR), which employs two nonparallel functions to identify the upper and lower sides of the interval output data. For analyzing data more flexible, we construct a so-called extended twin support vector regression (ETSVR) method, which concept utilizes two related TSVR functions to model the upper and lower ends of the interval data. In the meantime, we adopt the Particle Swarm Optimization (PSO) algorithm to reduce the searching burden for the tuning parameters of our algorithms. The analyzed results of the artificial and real data in this study show that the constructed interval models by ETSVR gives better performances than those based on ITSVR. HSU, HSIANG-LING 許湘伶 2018 學位論文 ; thesis 53 en_US |
collection |
NDLTD |
language |
en_US |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立高雄大學 === 統計學研究所 === 106 === For the past decade, generalized support vector regression methods have been applied to model the interval-valued data. Based on the idea of support vector regression (SVR) and twin support vector regression (TSVR), Peng et al. (2015) proposed a novel interval twin support vector regression (ITSVR), which employs two nonparallel functions to identify the upper and lower sides of the interval output data. For analyzing data more flexible, we construct a so-called extended twin support vector regression (ETSVR) method, which concept utilizes two related TSVR functions to model the upper and lower ends of the interval data. In the meantime, we adopt the Particle Swarm Optimization (PSO) algorithm to reduce the searching burden for the tuning parameters of our algorithms. The analyzed results of the artificial and real data in this study show that the constructed interval models by ETSVR gives better performances than those based on ITSVR.
|
author2 |
HSU, HSIANG-LING |
author_facet |
HSU, HSIANG-LING CHEN, CHIA-WEI 陳嘉緯 |
author |
CHEN, CHIA-WEI 陳嘉緯 |
spellingShingle |
CHEN, CHIA-WEI 陳嘉緯 Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
author_sort |
CHEN, CHIA-WEI |
title |
Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
title_short |
Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
title_full |
Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
title_fullStr |
Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
title_full_unstemmed |
Extended Twin Support Vector Regression via Particle Swarm Optimization for the Interval Data |
title_sort |
extended twin support vector regression via particle swarm optimization for the interval data |
publishDate |
2018 |
url |
http://ndltd.ncl.edu.tw/handle/4n36m4 |
work_keys_str_mv |
AT chenchiawei extendedtwinsupportvectorregressionviaparticleswarmoptimizationfortheintervaldata AT chénjiāwěi extendedtwinsupportvectorregressionviaparticleswarmoptimizationfortheintervaldata AT chenchiawei jīyúlìziqúnyōuhuàsuànfǎdeyánshēnshuāngzhīchíxiàngliànghuíguīyīngyòngyúqūjiānshùjùzīliào AT chénjiāwěi jīyúlìziqúnyōuhuàsuànfǎdeyánshēnshuāngzhīchíxiàngliànghuíguīyīngyòngyúqūjiānshùjùzīliào |
_version_ |
1719168747733778432 |