Learning minimum volume sets with support vector machines
| dc.citation.bibtexName | inproceedings | en_US |
| dc.citation.conferenceName | IEEE Workshop on Machine Learning for Signal Processing (MLSP) | en_US |
| dc.citation.firstpage | 301 | |
| dc.citation.lastpage | 306 | |
| dc.citation.location | Maynooth, Ireland | en_US |
| dc.contributor.author | Davenport, Mark A. | en_US |
| dc.contributor.author | Baraniuk, Richard G. | en_US |
| dc.contributor.author | Scott, Clayton D. | en_US |
| dc.date.accessioned | 2007-10-31T00:41:41Z | |
| dc.date.available | 2007-10-31T00:41:41Z | |
| dc.date.issued | 2006-09-01 | en |
| dc.date.modified | 2006-09-13 | en_US |
| dc.date.note | 2006-09-13 | en_US |
| dc.date.submitted | 2006-09-01 | en_US |
| dc.description | Conference Paper | en_US |
| 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. | en_US |
| dc.identifier.citation | M. A. Davenport, R. G. Baraniuk and C. D. Scott, "Learning minimum volume sets with support vector machines," 2006. | |
| dc.identifier.doi | http://dx.doi.org/10.1109/MLSP.2006.275565 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/19833 | |
| dc.language.iso | eng | |
| dc.title | Learning minimum volume sets with support vector machines | en_US |
| dc.type | Conference paper | |
| dc.type.dcmi | Text |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Dav2006Sep5Learningmi.PDF
- Size:
- 713.91 KB
- Format:
- Adobe Portable Document Format