.png){kind=icon}
Return
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
Abstract
En 中文
This paper proposes an accelerated decomposition algorithm for the robust support vector machine (SVM). Robust SVM aims at solving the overfitting problem when there is outlier in the training data set, which makes the decision surface less detored and results in sparse support vectors. Training of the robust SVM leads to a quadratic optimization problem with bound and linear constraint. Osuna provides a theorem which proves that the Standard SVM's quadratic programming (QP) problem can be broken down into a series of smaller QP subproblems. This paper derives the Kuhn-Tucker condition and decomposition algorithm for the robust SVM. Furthermore, a pre-selection technique is incorporated into the algorithm to speed up the calculation. The experiment using standard data sets shows that the accelerated decomposition algorithm makes the training process more efficient.
Keywords:
kernel method
robust support vector machine (SVM)
SVMs
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
Organization
No organization information available
Cited Papers
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
SaveShareSave
ShareSaveShareSaveShareSaveShareSave no more
no more