.png){kind=icon}
.png){kind=icon}
Return
The Proximal Trajectory Algorithm in SVM Cross Validation
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2015.2430935.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We propose a bilevel cross-validation scheme for support vector machine (SVM) model selection based on the construction of the entire regularization path. Since such path is a particular case of the more general proximal trajectory concept from nonsmooth optimization, we propose for its construction an algorithm based on solving a finite number of structured linear programs. Our methodology, differently from other approaches, works directly on the primal form of SVM. Numerical results are presented on binary data sets drawn from literature.
Keywords:
Cross validation (CV)
model selection
nonsmooth optimization
proximal trajectory
regularization path
support vector machine (SVM)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.5K
Citations:
7.2W