Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/7673
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gao, Junbin | en |
dc.contributor.author | Kwan, Paul H | en |
dc.contributor.author | Shi, Daming | en |
dc.date.accessioned | 2011-06-08T12:38:00Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Neural Networks, 23(2), p. 257-264 | en |
dc.identifier.issn | 1879-2782 | en |
dc.identifier.issn | 0893-6080 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/7673 | - |
dc.description.abstract | Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages. | en |
dc.language | en | en |
dc.publisher | Pergamon Press | en |
dc.relation.ispartof | Neural Networks | en |
dc.title | Sparse Kernel Learning with LASSO and Its Bayesian Inference Algorithm | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1016/j.neunet.2009.07.001 | en |
dc.subject.keywords | Numerical Computation | en |
dc.subject.keywords | Analysis of Algorithms and Complexity | en |
dc.subject.keywords | Neural, Evolutionary and Fuzzy Computation | en |
local.contributor.firstname | Junbin | en |
local.contributor.firstname | Paul H | en |
local.contributor.firstname | Daming | en |
local.subject.for2008 | 080205 Numerical Computation | en |
local.subject.for2008 | 080201 Analysis of Algorithms and Complexity | en |
local.subject.for2008 | 080108 Neural, Evolutionary and Fuzzy Computation | en |
local.subject.seo2008 | 890202 Application Tools and System Utilities | en |
local.subject.seo2008 | 899999 Information and Communication Services not elsewhere classified | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | Computer Science | en |
local.profile.email | jgao@une.edu.au | en |
local.profile.email | wkwan2@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.identifier.epublicationsrecord | une-20100414-144411 | en |
local.publisher.place | United Kingdom | en |
local.format.startpage | 257 | en |
local.format.endpage | 264 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 23 | en |
local.identifier.issue | 2 | en |
local.contributor.lastname | Gao | en |
local.contributor.lastname | Kwan | en |
local.contributor.lastname | Shi | en |
dc.identifier.staff | une-id:jgao | en |
dc.identifier.staff | une-id:wkwan2 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:7844 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Sparse Kernel Learning with LASSO and Its Bayesian Inference Algorithm | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Gao, Junbin | en |
local.search.author | Kwan, Paul H | en |
local.search.author | Shi, Daming | en |
local.uneassociation | Unknown | en |
local.identifier.wosid | 000274881700011 | en |
local.year.published | 2010 | en |
Appears in Collections: | Journal Article |
Files in This Item:
File | Description | Size | Format |
---|
SCOPUSTM
Citations
109
checked on Apr 27, 2024
Page view(s)
1,126
checked on Apr 28, 2024
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.