A Short Introduction to Learning with Kernels

dc.contributor.author	Schoelkopf, Bernhard
dc.contributor.author	Smola, Alexander
dc.date.accessioned	2015-12-13T23:07:13Z
dc.date.issued	2003
dc.date.updated	2015-12-12T08:08:18Z
dc.description.abstract	We briefly describe the main ideas of statistical learning theory, support vector machines, and kernel feature spaces. This includes a derivation of the support vector optimization problem for classification and regression, the ν-trick, various kernels a
dc.identifier.isbn	3540005293
dc.identifier.uri	http://hdl.handle.net/1885/86108
dc.publisher	Springer
dc.relation.ispartof	Advanced Lectures on Machine Learning
dc.relation.isversionof	1 Edition
dc.title	A Short Introduction to Learning with Kernels
dc.type	Book chapter
local.bibliographicCitation.lastpage	64
local.bibliographicCitation.placeofpublication	Germany
local.bibliographicCitation.startpage	41
local.contributor.affiliation	Schoelkopf, Bernhard, Max Planck Institute for Biological Cybernetics
local.contributor.affiliation	Smola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.authoruid	Smola, Alexander, u4039398
local.description.embargo	2037-12-31
local.description.notes	Imported from ARIES
local.description.refereed	Yes
local.identifier.absfor	080109 - Pattern Recognition and Data Mining
local.identifier.ariespublication	MigratedxPub14865
local.identifier.scopusID	2-s2.0-35248821683
local.type.status	Published Version

Downloads

Now showing 1 - 1 of 1