Randomized Algorithms for Large scale SVMs

Details Randomized Algorithms for Large scale SVMs Randomized Algorithms for Large scale SVMs

2009-09-19

arxiv.org/abs/0909.3609v1

Computer Science

Learning

17 pages, Submitted to Machine Learning journal (October 2008) - under revision

Scientific paper

We propose a randomized algorithm for training Support vector machines(SVMs) on large datasets. By using ideas from Random projections we show that the combinatorial dimension of SVMs is $O({log} n)$ with high probability. This estimate of combinatorial dimension is used to derive an iterative algorithm, called RandSVM, which at each step calls an existing solver to train SVMs on a randomly chosen subset of size $O({log} n)$. The algorithm has probabilistic guarantees and is capable of training SVMs with Kernels for both classification and regression problems. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up existing SVM learners, without loss of accuracy.

Affiliated with

Also associated with

No associations

Rating

Randomized Algorithms for Large scale SVMs 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 Randomized Algorithms for Large scale SVMs, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Randomized Algorithms for Large scale SVMs will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-295984