.png){kind=icon}
Return
An ACO-based algorithm for parameter optimization of support vector machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2010.03.067.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVM model to solve this problem. The proposed algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Ant colony optimization (ACO) algorithm
Support vector machines (SVM)
Parameter optimization
ACO-SVM model
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
A hybrid approach for feature subset selection using neural networks and ant colony optimization
.png){kind=icon}
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
ShareSave
View more