Loading...
Please wait, while we are loading the content...
Similar Documents
Parameter Tuning in Support Vector Regression for Large Scale Problems
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ryu, Jee-Youl Kwak, Minjung Yoon, Min |
| Copyright Year | 2015 |
| Abstract | In support vector machine, the values of parameters included in kernels affect strongly generalization ability. It is often difficult to determine appropriate values of those parameters in advance. It has been observed through our studies that the burden for deciding the values of those parameters in support vector regression can be reduced by utilizing ensemble learning. However, the straightforward application of the method to large scale problems is too time consuming. In this paper, we propose a method in which the original data set is decomposed into a certain number of sub data set in order to reduce the burden for parameter tuning in support vector regression with large scale data sets and imbalanced data set, particularly. |
| Starting Page | 15 |
| Ending Page | 21 |
| Page Count | 7 |
| File Format | PDF HTM / HTML |
| DOI | 10.5391/JKIIS.2015.25.1.015 |
| Volume Number | 25 |
| Alternate Webpage(s) | http://ocean.kisti.re.kr/downfile/volume/kfis/PJJNBT/2015/v25n1/PJJNBT_2015_v25n1_15.pdf |
| Alternate Webpage(s) | https://doi.org/10.5391/JKIIS.2015.25.1.015 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
