Title :
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
Author :
Keerthi, S. Sathiya
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
fDate :
9/1/2002 12:00:00 AM
Abstract :
The paper discusses implementation issues related to the tuning of the hyperparameters of a support vector machine (SVM) with L2 soft margin, for which the radius/margin bound is taken as the index to be minimized, and iterative techniques are employed for computing radius and margin. The implementation is shown to be feasible and efficient, even for large problems having more than 10000 support vectors.
Keywords :
data analysis; iterative methods; learning automata; minimisation; pattern classification; L2 soft margin; SVM hyperparameter tuning; iterative algorithms; iterative techniques; radius/margin bound; support vector machine; support vectors; Algorithm design and analysis; Helium; Iterative algorithms; Kernel; Large-scale systems; Mechanical engineering; Polynomials; Quadratic programming; Support vector machine classification; Support vector machines;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.1031955
