Show simple item record

dc.contributor.authorDavenport, Mark A.
Baraniuk, Richard G.
Scott, Clayton D.
dc.creatorBaraniuk, Richard G.
Scott, Clayton D.
Davenport, Mark A.
dc.date.accessioned 2007-10-31T00:41:41Z
dc.date.available 2007-10-31T00:41:41Z
dc.date.issued 2006-09-01
dc.date.submitted 2006-09-01
dc.identifier.citation M. A. Davenport, R. G. Baraniuk and C. D. Scott, "Learning minimum volume sets with support vector machines," 2006.
dc.identifier.urihttps://hdl.handle.net/1911/19833
dc.description Conference Paper
dc.description.abstract Given a probability law P on d-dimensional Euclidean space, the minimum volume set (MV-set) with mass beta , 0 < beta < 1, is the set with smallest volume enclosing a probability mass of at least beta. We examine the use of support vector machines (SVMs) for estimating an MV-set from a collection of data points drawn from P, a problem with applications in clustering and anomaly detection. We investigate both one-class and two-class methods. The two-class approach reduces the problem to Neyman-Pearson (NP) classification, where we artificially generate a second class of data points according to a uniform distribution. The simple approach to generating the uniform data suffers from the curse of dimensionality. In this paper we (1) describe the reduction of MV-set estimation to NP classification, (2) devise improved methods for generating artificial uniform data for the two-class approach, (3) advocate a new performance measure for systematic comparison of MV-set algorithms, and (4) establish a set of benchmark experiments to serve as a point of reference for future MV-set algorithms. We find that, in general, the two-class method performs more reliably.
dc.language.iso eng
dc.title Learning minimum volume sets with support vector machines
dc.type Conference paper
dc.date.note 2006-09-13
dc.citation.bibtexName inproceedings
dc.date.modified 2006-09-13
dc.citation.location Maynooth, Ireland
dc.citation.conferenceName IEEE Workshop on Machine Learning for Signal Processing (MLSP)
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/MLSP.2006.275565
dc.citation.firstpage 301
dc.citation.lastpage 306


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1336]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

Show simple item record