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 :
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