Arthur Nádas
IEEE Transactions on Neural Networks
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.
Arthur Nádas
IEEE Transactions on Neural Networks
Cristina Cornelio, Judy Goldsmith, et al.
JAIR
Joxan Jaffar
Journal of the ACM
S. Winograd
Journal of the ACM