The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
A Stochastic Optimization Approach for Parameter Tuning of Support Vector Machines
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Ant colony optimization theory: a survey
Theoretical Computer Science
Optimizing resources in model selection for support vector machine
Pattern Recognition
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
Computers and Operations Research
A heuristic approach to find the global optimum of function
Journal of Computational and Applied Mathematics
Local prediction of non-linear time series using support vector regression
Pattern Recognition
Expert Systems with Applications: An International Journal
Evolutionary tuning of multiple SVM parameters
Neurocomputing
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Tuning n-gram string kernel SVMs via meta learning
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
Applying Ant Colony Optimization to configuring stacking ensembles for data mining
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.