Linked Life Data

17550114

Source:http://linkedlifedata.com/resource/pubmed/id/17550114

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-6-6
pubmed:abstractText
Given the sequential data from an unknown target system with changing parameters, the first part of this paper discusses online algorithms that adapt to smooth as well as abrupt changes. This paper examines four different parameter/ hyperparameter dynamics for online learning and compares their performance within an online Bayesian learning framework. Using the dynamics that performed best in the first part, the second part of this paper attempts to perform change detection in unknown systems in terms of the time dependence of the marginal likelihood. Because of the sequential nature of the algorithms, a sequential Monte Carlo scheme (particle filter) is a natural means for implementation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1083-4419
pubmed:author
pubmed:issnType
Print
pubmed:volume
37
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
592-606
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Adaptation and change detection with a sequential Monte Carlo scheme.
pubmed:affiliation
Graduate School of Science and Engineering, Waseda University, Tokyo, Japan. takashi@mse.waseda.ac.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't