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ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
14 years 7 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng
JSAC
2006
120views more  JSAC 2006»
14 years 9 months ago
Multiple-Source Internet Tomography
Abstract-- Information about the topology and link-level characteristics of a network is critical for many applications including network diagnostics and management. However, this ...
Michael Rabbat, Mark Coates, Robert D. Nowak
ECAI
2004
Springer
15 years 3 months ago
Combining Multiple Answers for Learning Mathematical Structures from Visual Observation
Learning general truths from the observation of simple domains and, further, learning how to use this knowledge are essential capabilities for any intelligent agent to understand ...
Paulo Santos, Derek R. Magee, Anthony G. Cohn, Dav...
ICANN
2011
Springer
14 years 1 months ago
Learning from Multiple Annotators with Gaussian Processes
Abstract. In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, po...
Perry Groot, Adriana Birlutiu, Tom Heskes
ICASSP
2009
IEEE
15 years 4 months ago
Two microphone based direction of arrival estimation for multiple speech sources using spectral properties of speech
A two microphone direction of arrival (DOA) estimation technique for multiple speech sources is developed which exploits speech specific properties, namely sparsity in time-frequ...
Wenyi Zhang, Bhaskar D. Rao