Adaptive and Iterative Training Algorithm of Least Square Support Vector Machine Regression

FONT face; Verdana; YANG Bin; YANG Xiao-wei; HUAGN Lan; LIANG Yan-chun; ZHOU Chun-guang; WU Chun-guo; FONT

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Adaptive and Iterative Training Algorithm of Least Square Support Vector Machine Regression

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- Adaptive and Iterative Training Algorithm of Least Square Support Vector Machine Regression
- Acta Electronica Sinica Vol. 38, Issue 7, Pages: 1621-1625(2010)
- 作者机构：
  
  1. 吉林大学计算机科学与技术学院, 国家教育部符号计算与知识工程重点实验室,吉林,长春,130012
  2. 华南理工大学理学院数学系,广东,广州,510640
  3. 中国科学院自动化研究所模式识别国家重点实验室,北京,100080
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：2010
- 稿件说明：
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FONT face, Verdana, YANG Bin, et al. Adaptive and Iterative Training Algorithm of Least Square Support Vector Machine Regression[J]. Acta Electronica Sinica, 2010, 38(7): 1621-1625.
DOI：

FONT face, Verdana, YANG Bin, et al. Adaptive and Iterative Training Algorithm of Least Square Support Vector Machine Regression[J]. Acta Electronica Sinica, 2010, 38(7): 1621-1625. DOI：

摘要

<FONT face=Verdana>基于最小二乘支持向量机回归算法，本文在前期工作的基础上进行了扩展，提出了更加详尽的自适应迭代最小二乘支持向量机回归算法. 与标准的LSSVR相比，本文提出的算法在学习新样本的时候利用了已有的学习结果，可以快速获得新的学习机. 模拟结果表明，自适应迭代最小二乘支持向量机回归算法能够自适应地确定支持向量的数目，保留了QP方法在训练SVM时支持向量的稀疏性，在相近的回归精度下，该算法极大地提高了标准LSSVR学习的速度.

Abstract

<FONT face=Verdana>A novel adaptive and iterative training algorithm of least square support vector machine regression (AILSSVR) is presented based on the least square support vector machine regression (LSSVR) and the proof for inverse of an order-reduced matrix is given. Compared to the standard LSSVR

the proposed algorithm takes advantage of the previous training results when learning from a new sample; therefore

it can obtain a new learning machine efficiently. Numerical simulations demonstrate that AILSSVR can adaptively decide the number of support vectors and preserve the sparse property of support vectors in QP training methods. Under the similar regression accuracy

AILSSVR can speed up the learning process remarkably.

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