DocumentCode
514208
Title
Online semi-supervised learning: Application to dynamic learning from RADAR data
Author
Yver, B.
Author_Institution
CEA, CESTA, Le Barp, France
fYear
2009
fDate
12-16 Oct. 2009
Firstpage
1
Lastpage
6
Abstract
Dynamic learning from RADAR data is a new challenge which needs the development of new classification methods using an online semi-supervised learning approach. Except a very recent paper presented at ECML 2008, no algorithm in the literature can deal with this problem. Based on a theoretical analysis of the limitations and advantages of several semi-supervised and online learning methods, we proposed four algorithms as potential solutions to the dynamic learning problem which will be tested in next monthes.
Keywords
learning (artificial intelligence); radar computing; ECML; dynamic learning; online semisupervised learning; radar data; Algorithm design and analysis; Data mining; Databases; Learning systems; Radar applications; Radar measurements; Semisupervised learning; Supervised learning; Testing; Time measurement; classification; data flow; dynamic learning; online semi-supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Radar Conference - Surveillance for a Safer World, 2009. RADAR. International
Conference_Location
Bordeaux
Print_ISBN
978-2-912328-55-7
Type
conf
Filename
5438391
Link To Document