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» Learning and Generalization with the Information Bottleneck
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SIGIR
2011
ACM
14 years 22 days ago
Learning to rank from a noisy crowd
We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
Abhimanu Kumar, Matthew Lease
AAAI
2010
14 years 11 months ago
Multi-Task Active Learning with Output Constraints
Many problems in information extraction, text mining, natural language processing and other fields exhibit the same property: multiple prediction tasks are related in the sense th...
Yi Zhang 0010
WWW
2007
ACM
15 years 10 months ago
Understanding web search via a learning paradigm
Investigating whether one can view Web searching as a learning process, we examined the searching characteristics of 41 participants engaged in 246 searching tasks. We classified ...
Bernard J. Jansen, Brian Keith Smith, Danielle L. ...
CIKM
2009
Springer
15 years 4 months ago
Learning to rank graphs for online similar graph search
Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chem...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles
SIGIR
2010
ACM
15 years 1 months ago
Adaptive near-duplicate detection via similarity learning
In this paper, we present a novel near-duplicate document detection method that can easily be tuned for a particular domain. Our method represents each document as a real-valued s...
Hannaneh Hajishirzi, Wen-tau Yih, Aleksander Kolcz