Intelligent classification algorithms in enhancing the performance of support vector machine
Performing feature subset and tuning support vector machine (SVM) parameter processes in parallel with the aim to increase the classification accuracy is the current research direction in SVM. Common methods associated in tuning SVM parameters will discretize the continuous value of these parameters...
Saved in:
Main Authors: | Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana |
---|---|
Format: | Article |
Language: | English |
Published: |
Little Lion Scientific
2019
|
Subjects: | |
Online Access: | http://repo.uum.edu.my/27867/1/JIAIT%2097%202%202019%20664%20657.pdf http://repo.uum.edu.my/27867/ http://www.jatit.org/volumes/ninetyseven2.php |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter
by: Alwan, Hiba Basim, et al.
Published: (2017) -
Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
by: Alwan, Hiba Basim, et al.
Published: (2013) -
Optimizing support vector machine parameters using continuous ant colony optimization
by: Alwan, Hiba Basim, et al.
Published: (2012) -
Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization
by: Alwan, Hiba Basim, et al.
Published: (2013) -
Formulating new enhanced pattern classification algorithms based on ACO-SVM
by: Alwan, Hiba Basim, et al.
Published: (2013)