.png){kind=icon}
Return
Sparse ε-tube support vector regression by active learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-013-1131-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A method for the sparse solution of -tube support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on its influence on the accuracy of the model using the active learning principle. The training time can be adjusted by the user by selecting how often the hyper-parameters of the algorithm are optimised. The advantages of the proposed method are illustrated on several examples. The algorithm performance is compared with the performance of several state-of-the-art algorithms on the well-known benchmark data sets. The application of the proposed algorithm for the black-box modelling of the electronic circuits is also demonstrated. The experiments clearly show that it is possible to reduce the number of support vectors and significantly improve the accuracy versus complexity ratio of -tube support vector regression.
Keywords:
Support vector machines
Support vector regression
Sparse regression models
Active learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveElastic anisotropy and micro-damage processes in polycrystalline ice Part I: Theoretical formulation
PREAI
ShareSave
View more