Active learning in very large databases

Authors:
Navneet Panda;King-Shy Goh;Edward Y. Chang
Affiliations:
University of California, Santa Barbara, USA 93106;University of California, Santa Barbara, USA 93106;University of California, Santa Barbara, USA 93106
Venue:
Multimedia Tools and Applications
Year:
2006

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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.