Active learning in very large databases
Inhaltsverzeichnis
Referenz
N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006.
DOI
http://dx.doi.org/10.1007/s11042-006-0043-1
Abstract
Query-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.
Extended Abstract
Bibtex
@Article{Panda2006,
author="Panda, Navneet and Goh, King-Shy and Chang, Edward Y.",
title="Active learning in very large databases",
journal="Multimedia Tools and Applications",
year="2006",
volume="31",
number="3",
pages="249--267",
issn="1573-7721",
doi="10.1007/s11042-006-0043-1",
url="http://dx.doi.org/10.1007/s11042-006-0043-1 http://de.evo-art.org/index.php?title=Active_learning_in_very_large_databases"
}
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Links
Full Text
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.5446&rep=rep1&type=pdf
