On-Line LS-SVM Regression with Pruned Support Vectors - based on cross validatory choice of hyper-parameters

• 발행기관: 한국자료분석학회
• 저자명: 김대학(Daehak Kim)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.7 No.5, 1539~1546쪽, 전체 8쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2005.10.30

영문 초록

LS-SVM regression is known to be a good substitute for the traditional statistical regression method. LS-SVM is an SVM(support vector machine) version which involves equality constraints instead of inequality constraints and works with a squared loss function, which leads the solution to be obtained from a linear Karush-Kuhn-Tucker conditions instead of a quadratic programming problem. But computational difficulties are remained to operate the inversion of matrix of large data set. For the analysis of the on-line data set or the large data sets, we suggest an on-line LS-SVM regression with pruning support vectors and modifying the hyper-parameters in each step. In numerical studies we show that with relatively small number of pruned support vectors almost same prediction performance can be obtained as a batch LS-SVM regression in a sense of MSE.

목차

1. Introduction

2. LS-SVM Regression

3. On-Line LS-SVM Regression with Pruning SVs

4. Numerical Study

5. Concluding Remarks

References

키워드

LS-SVM Pruned support vector Cross validation Kernel function Batch mode Mean squares errors Hyper-parameters