Seung Gu Kang, Jeff Weber, et al.
ACS Fall 2023
In this article we describe and analyze sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sublinear-time algorithms were not known before. These new algorithms use a combination of a novel sampling techniques and a new multiplicative update algorithm. We give lower bounds which show the running times of many of our algorithms to be nearly best possible in the unit-cost RAM model. © 2012 ACM 0004-5411/2012/10-ART23.
Seung Gu Kang, Jeff Weber, et al.
ACS Fall 2023
Joseph Y. Halpern
aaai 1996
Khalid Abdulla, Andrew Wirth, et al.
ICIAfS 2014
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence