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Journal of Software:2017.28(12):3146-3155

最小二乘孪生参数化不敏感支持向量回归机
丁世飞,黄华娟
(中国矿业大学 计算机科学与技术学院, 江苏 徐州 221116;中国科学院 计算技术研究所 智能信息处理重点实验室, 北京 100190;中国矿业大学 计算机科学与技术学院, 江苏 徐州 221116;中国科学院 计算技术研究所 智能信息处理重点实验室, 北京 100190;广西民族大学 信息科学与工程学院, 广西 南宁 530006)
Least Squares Twin Parametric Insensitive Support Vector Regression
DING Shi-Fei,HUANG Hua-Juan
(School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, The Chinese Academy of Science, Beijing 100190, China;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, The Chinese Academy of Science, Beijing 100190, China;College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China)
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Received:January 10, 2016    Revised:October 08, 2016
> 中文摘要: 孪生参数化不敏感支持向量回归机(twin parametric insensitive support vector regression,简称TPISVR)是一种新型机器学习方法.与其他回归方法相比,TPISVR在处理异方差噪声方面具有独特的优势.标准TPISVR的训练算法可以归结为在对偶空间求解一对具有不等式约束的二次规划问题.然而,这种求解方法的时间消耗比较大.引入最小二乘思想,将TPISVR的两个二次规划问题转化为两个线性方程组,并在原始空间上直接求解,提出了最小二乘孪生参数化不敏感支持向量回归机(least squares TPISVR,简称LSTPISVR).为了解决LSTPISVR的参数选择问题,提出了混沌布谷鸟优化算法,并用其对LSTPISVR的参数进行优化选择.在人工数据集和UCI数据集上的实验结果表明:LSTPISVR在保持精度不下降的情况下,具有更高的运行效率.
Abstract:Twin parametric insensitive support vector regression(TPISVR) is a novel machine learning method proposed. Compared to other regression methods, TPISVR has unique advantages in dealing with heteroscedastic noise. Standard TPISVR can be attributed to solve a pair of quadratic programming problem(QPP) with inequality constraints in the dual space. However, this method is subject to the constraints of time and memory when number of samples are large. This paper introduces the least squares ideas, and proposes the least squares twin parametric insensitive support vector regression(LSTPISVR) which transforms the two QPPs of TPISVR into linear equations and solves them directly on the original space. Further, a chaotic cuckoo optimization algorithm is introduced for parameter selection of LSTPISVR. Experiments on artificial datasets and UCI datasets show that LSTPISVR not only has fast learning speed, but also shows good generalization performance.
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基金项目:国家自然科学基金(61379101,61662005,61672522);国家重点基础研究发展计划(973)(2013CB329502) 国家自然科学基金(61379101,61662005,61672522);国家重点基础研究发展计划(973)(2013CB329502)
Foundation items:National Natural Science Foundation of China (61379101, 61662005, 61672522); National Basic Research Program of China (973) (2013CB329502)
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丁世飞,黄华娟.最小二乘孪生参数化不敏感支持向量回归机.软件学报,2017,28(12):3146-3155

DING Shi-Fei,HUANG Hua-Juan.Least Squares Twin Parametric Insensitive Support Vector Regression.Journal of Software,2017,28(12):3146-3155