Name
Abstract | ||
|---|---|---|
A novel Support Vector Regression(SVR) algorithm has been proposed recently by us. This approach, called Lagrangian Support Vector Regression(LSVR), is an reformulation on the standard linear support vector regression, which leads to the minimization problem of an unconstrained differentiable convex function. During the process of computing, the inversion of matrix after incremented is solved based on the previous results, therefore it is not necessary to relearn the whole training set to reduce the computation process. In this paper, we implemented the LSVR and tested it on Mackey-Glass time series to compare the performances of different algorithms. According to the experiment results, we achieve a high-quality prediction about time series. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_99 | ISNN (3) |
Keywords | Field | DocType |
different algorithm,time series,novel support vector regression,mackey-glass time series,predicting time series,incremental langrangian support,minimization problem,experiment result,high-quality prediction,computation process,lagrangian support,vector regression,convex function,support vector regression | Training set,Mathematical optimization,Lagrangian,Matrix (mathematics),Inversion (meteorology),Computer science,Support vector machine,Convex function,Differentiable function,Artificial intelligence,Machine learning,Computation | Conference |
Volume | ISSN | Citations |
4493 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Duan | 1 | 110 | 19.58 |
| Weizhen Hou | 2 | 17 | 6.02 |
| Guoping He | 3 | 91 | 13.59 |
| Qingtian Zeng | 4 | 242 | 43.67 |