New Support Vector Machine with Multi-Input and Multi-Output Based on Adaptive Grouping

doi:10.7652/xjtuxb201306009

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New Support Vector Machine with Multi-Input and Multi-Output Based on Adaptive Grouping

更新时间：2025-05-01

- New Support Vector Machine with Multi-Input and Multi-Output Based on Adaptive Grouping
- Vol. 47, Issue 6, Pages: 50-54+72(2013)
- 作者机构：
  
  河南师范大学计算机与信息工程学院,河南,新乡,453007
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201306009
  CLC： TP181
- Online First：10 June 2013，
  
  Published：2013
- 稿件说明：
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New Support Vector Machine with Multi-Input and Multi-Output Based on Adaptive Grouping[J]. 2013, 47(6): 50-54+72.
DOI：

New Support Vector Machine with Multi-Input and Multi-Output Based on Adaptive Grouping[J]. 2013, 47(6): 50-54+72. DOI： 10.7652/xjtuxb201306009.

摘要

针对不同输出端之间相关程度的差异对多输入多输出回归模型泛化能力的影响

提出了一种基于自适应分组的多输入多输出支持向量机算法。该算法基于相关性强的输出端其模型参数也较相似的假设

首先在多维支持向量机的基础上引入带分组结构的正则项

进而将上述正则化问题转变为混合0-1规划; 其次

采用交替优化的方法

使相关性强的输出端在同一个分组内进行独立训练

最终自适应地识别最优分组结构和模型参数。分别采用仿真数据和圆柱壳振动工程数据对所提算法进行测试

结果表明

该算法可有效辨识出输出端的相关度

与传统算法相比

该算法可有效提高支持向量机回归模型的泛化能力。

Abstract

Recently

multi-dimensional support vector regression(M-SVR)has been a promising tool in solving a wide range of multi-input and multi-output(MIMO)regression problems. However

when some output dimensions have more dependencies than others

M-SVR is are generally hard to obtain impressive performance due to the negative or redundant domain knowledge across all outputs. A structure of adaptive grouping is introduced into the M-SVR to solve the problem. It is assumed that model parameters of related output dimensions are similar to each other

then a new regularization-based M-SVR algorithm is presented by introducing a regularizer with grouping structure. The regularization problem is then converted into a mixed 0-1 programming problem. Alternating optimization is employed for learning related outputs jointly in the same group

and to obtain the optimal grouping structure and model parameters through adaptively identifying the grouping structure. The proposed algorithm is tested empirically on a toy problem as well as a real-life data set. The experimental results show that the performance of the proposed algorithm outperforms the performance of the classical M-SVR and the single-output SVM algorithm.

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references

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