Sublinear Optimization for Machine Learning

Details Sublinear Optimization for Machine Learning Sublinear Optimization for Machine Learning

2010-10-21

arxiv.org/abs/1010.4408v1

Computer Science

Learning

extended abstract appeared in FOCS 2010

Scientific paper

We give 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. We also give implementations of our algorithms in the semi-streaming setting, obtaining the first low pass polylogarithmic space and sublinear time algorithms achieving arbitrary approximation factor.

Affiliated with

Also associated with

No associations

Rating

Sublinear Optimization for Machine Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Sublinear Optimization for Machine Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sublinear Optimization for Machine Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-423185