Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Kernel methods such as the support vector machine are one of the most successful algorithms in modern machine learning. Their advantage is that linear algorithms are extended to non-linear scenarios in a straightforward way by the use of the kernel trick. However, naive use of kernel methods is computationally expensive since the computational complexity typically scales cubically with respect to the number of training samples. In this article, we review recent advances in the kernel methods, with emphasis on scalability for massive problems. Copyright © 2009 The Institute of Electronics, Information and Communication Engineers.
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Wang Zhang, Subhro Das, et al.
ICASSP 2025
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence