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ALT
2009
Springer
14 years 7 months ago
Average-Case Active Learning with Costs
Abstract. We analyze the expected cost of a greedy active learning algorithm. Our analysis extends previous work to a more general setting in which different queries have differe...
Andrew Guillory, Jeff A. Bilmes
IJSI
2008
156views more  IJSI 2008»
13 years 10 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
SIGMOD
2010
ACM
308views Database» more  SIGMOD 2010»
13 years 8 months ago
GBLENDER: towards blending visual query formulation and query processing in graph databases
Given a graph database D and a query graph g, an exact subgraph matching query asks for the set S of graphs in D that contain g as a subgraph. This type of queries find important...
Changjiu Jin, Sourav S. Bhowmick, Xiaokui Xiao, Ja...
ECCV
2008
Springer
15 years 7 days ago
Improving Shape Retrieval by Learning Graph Transduction
Abstract. Shape retrieval/matching is a very important topic in computer vision. The recent progress in this domain has been mostly driven by designing smart features for providing...
Xingwei Yang, Xiang Bai, Longin Jan Latecki, Zhuow...
IAT
2010
IEEE
13 years 8 months ago
Selecting Operator Queries Using Expected Myopic Gain
When its human operator cannot continuously supervise (much less teleoperate) an agent, the agent should be able to recognize its limitations and ask for help when it risks making...
Robert Cohn, Michael Maxim, Edmund H. Durfee, Sati...