Active learning in very large databases

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.

• Type: Journal
• Year: 2006
• Where: MTA
• Authors: Navneet Panda, Kingshy Goh, Edward Y. Chang
• Keywords: Kernel Methods, MTA 2006, Multiple Semantics, Variable Interpretations