A new support vector machine with an optimal additive kernel

Neurocomputing, pp. 279-299, 2019.

Cited by: 1|Views7
EI

Abstract:

Although the kernel support vector machine (SVM) outperforms linear SVM, its application to real world problems is limited because the evaluation of its decision function is computationally very expensive due to kernel expansion. On the other hand, additive kernel (AK) SVM enables fast evaluation of a decision function using look-up table...More

Code:

Data:

Your rating :
0

 

Tags
Comments