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CVPR
2000
IEEE
16 years 8 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
ICML
2007
IEEE
16 years 7 months ago
Information-theoretic metric learning
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...
ICML
2008
IEEE
16 years 7 months ago
Random classification noise defeats all convex potential boosters
A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class...
Philip M. Long, Rocco A. Servedio
ISBI
2008
IEEE
16 years 7 months ago
Fast displacement probability profile approximation from HARDI using 4th-order tensors
Cartesian tensor basis have been widely used to approximate spherical functions. In Medical Imaging, tensors of various orders have been used to model the diffusivity function in ...
Angelos Barmpoutis, Baba C. Vemuri, John R. Forder
EWSN
2009
Springer
16 years 7 months ago
MVSink: Incrementally Building In-Network Aggregation Trees
Abstract. In-network data aggregation is widely recognized as an acceptable means to reduce the amount of transmitted data without adversely affecting the quality of the results. T...
Leonardo L. Fernandes, Amy L. Murphy