Active learning in very large databases

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N. Panda, K. Goh, and E. Y. Chang: Active learning in very large databases. MTAP, 31(3):249-267, 2006.



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


author="Panda, Navneet and Goh, King-Shy and Chang, Edward Y.",
title="Active learning in very large databases",
journal="Multimedia Tools and Applications",

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