[1]黄华娟,韦修喜,周永权.光滑孪生参数化不敏感支持向量回归机[J].郑州大学学报(工学版),2022,43(02):28-34.[doi:10.13705/j.issn.1671-6833.2022.02.005]
　HUANG Huajuan,WEI Xiuxi,ZHOU Yongquan.Smooth Twin Parametric Insensitive Support Vector Regression[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):28-34.[doi:10.13705/j.issn.1671-6833.2022.02.005]
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光滑孪生参数化不敏感支持向量回归机()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年02期
页码:
28-34
栏目:
出版日期:
2022-02-27

文章信息/Info

Title:
Smooth Twin Parametric Insensitive Support Vector Regression
作者:
黄华娟1; 韦修喜1; 周永权1; 2
1.广西民族大学人工智能学院;2.广西民族大学广西混杂计算与集成电路设计分析重点实验室;

Author(s):
HUANG Huajuan1;  WEI Xiuxi1;  ZHOU Yongquan1; 2
1.College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China; \
2.Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China
关键词:
Keywords:
twin parametric insensitive support vector regression;  smooth technology;  heteroscedastic noise;  Newton method;  training efficiency
分类号:
TP18
DOI:
10.13705/j.issn.1671-6833.2022.02.005
文献标志码:
A
摘要:
作为机器学习方法之一的孪生参数化不敏感支持向量回归机（Twin parametric insensitive support vector regression, TPISVR）有着简洁的数学模型，良好的学习性能，特别适合于求解带有结构异方差噪声的数据回归问题。然而，TPISVR的训练速度较低，训练效率有待提高。在本文中，引入光滑函数和正则项，将TPISVR的数学模型转化为两个无约束的极小化问题，从而可以通过具有快速求解能力的Newton法进行求解，提出光滑孪生参数化不敏感支持向量回归机（Smooth twin parametric insensitive support vector regression, STPISVR）。在人工数据集和UCI数据集上的实验结果表明，和其他机器学习方法相比，STPISVR在保证精度不下降的情况下，获得了更高的训练效率。
Abstract:
As one of the machine learning methods, twin parametric insensitive support vector regression (TPISVR) had a simple mathematical model and good learning performance. It was especially suitable for solving data regression problems with structural heteroscedasticity noise. However, the training speed of TPISVR was low, and the training efficiency needs to be improved. The traditional algorithm of TPISVR could be reduced to solve two quadratic programming problems with inequality constraints by transforming dual problems. However, this method of solving quadratic programming problems with large number of samples would be restricted by time and memory, which was the key to the low training efficiency of TPISVR. In this study, the positive sign function was introduced to transform the two quadratic programming problems of TPISVR into two non-differentiable unconstrained optimization problems. Secondly, CHKS smooth function and regular term were introduced to regularize TPISVR model, and smooth approximation was made to the non-differentiable unconstrained optimization problem, so as to transform the non-differentiable model into a differentiable unconstrained optimization problem. The new model was solved by Newton Armijo method with fast convergence speed, and a smooth twin parameterized insensitive support vector regression machine (STPISVR) was proposed. Finally, it was proved theoretically that STPISVR model was convergent and had arbitrary order smoothness; In order to verify the effectiveness and feasibility of the algorithm, simulation experiments were carried out on the artificial data set and UCI data set commonly used in machine learning. The experimental results showed that compared with other machine learning methods, STPISVR achieved higher training efficiency without reducing the accuracy.

参考文献/References:

[1] CORTES C,VAPNIK V.Support-vector networks[J].Machine learning,1995,20(3):273-297.

[2] MELLO A R,STEMMER M R,KOERICH A L.Incremental and decremental fuzzy bounded twin support vector machine[J].Information sciences,2020,526:20-38.

更新日期/Last Update: 2022-02-25