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Active learning in very large databases
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DOI：10.1007/s11042-006-0043-1.png)
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摘要
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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.
Keyword:
active learning
image retrieval
relevance feedback
support vector machines
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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