Title :
Online semi-supervised learning: Application to dynamic learning from RADAR data
Author_Institution :
CEA, CESTA, Le Barp, France
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;
Conference_Titel :
Radar Conference - Surveillance for a Safer World, 2009. RADAR. International
Conference_Location :
Bordeaux
Print_ISBN :
978-2-912328-55-7