RT Journal Article
T1 Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification
A1 Isla-Cernadas, Diego
A1 Fernández-Delgado, Manuel
A1 Sirsat, Manisha S.
A1 Cernadas García, Eva
A1 Maarouf, Haitham
A1 Barro Ameneiro, Senén
K1 Classification
K1 Efficient computing
K1 Large-scale datasets
K1 Model selection
K1 Radial basis kernel
K1 Support vector machine (SVM)
AB The support vector machine (SVM) with Gaussian kernel often achieves state-of-the-art performance in classification problems, but requires the tuning of the kernel spread. Most optimization methods for spread tuning require training, being slow and not suited for large-scale datasets. We formulate an analytic expression to calculate, directly from data without iterative search, the spread minimizing the difference between Gaussian and ideal kernel matrices. The proposed direct gamma tuning (DGT) equals the performance of and is one to two orders of magnitude faster than the state-of-the art approaches on 30 small datasets. Combined with random sampling of training patterns, it also runs on large classification problems. Our method is very efficient in experiments with 20 large datasets up to 31 million of patterns, it is faster and performs significantly better than linear SVM, and it is also faster than iterative minimization. Code is available upon paper acceptance from this link: http://persoal.citius.usc.es/ manuel.fernandez.delgado/papers/dgt/index.html and from CodeOcean: https://codeocean.com/capsule/4271163/tree/v1.
PB IEEE
SN 2162-237X
YR 2024
FD 2024
LK http://hdl.handle.net/10347/34260
UL http://hdl.handle.net/10347/34260
LA eng
NO D. Isla-Cernadas, M. Fernández-Delgado, E. Cernadas, M. S. Sirsat, H. Maarouf and S. Barro, "Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3377370
DS Minerva
RD 30 abr 2026