Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Classification in a Normalized Feature Space using Support Vector Machines

Simple item page
dc.contributor.authorGraf, Arnulf
dc.contributor.authorSmola, Alexander
dc.contributor.authorBorer, Silvio
dc.date.accessioned2015-12-13T23:07:04Z
dc.date.issued2003
dc.date.updated2015-12-12T08:07:51Z
dc.description.abstractThis paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real-world datasets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.
dc.identifier.issn1045-9227
dc.identifier.urihttp://hdl.handle.net/1885/86049
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Neural Networks
dc.subjectKeywords: Algorithms; Error analysis; Feature extraction; Linear programming; Mathematical models; Mathematical transformations; Matrix algebra; Numerical methods; Optimization; Spurious signal noise; Vectors; Classification error; Feature space; Optimal separating Classification error; Dataset partitioning; Feature space; Input space; Noise; Normalization; Optimal separating hyperplane (OSH); Optimality; Support vector machines (SVMs)
dc.titleClassification in a Normalized Feature Space using Support Vector Machines
dc.typeJournal article
local.bibliographicCitation.issue3
local.bibliographicCitation.lastpage605
local.bibliographicCitation.startpage597
local.contributor.affiliationGraf, Arnulf, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.affiliationBorer, Silvio, Swiss Federal Institute of Technology (ETH)
local.contributor.authoruidSmola, Alexander, u4039398
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub14787
local.identifier.citationvolume14
local.identifier.doi10.1109/TNN.2003.811708
local.identifier.scopusID2-s2.0-0037844872
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
01_Graf_Classification_in_a_Normalized_2003.pdf
Size:
1.03 MB
Format:
Adobe Portable Document Format
Download

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS