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ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 1 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
NN
2006
Springer
163views Neural Networks» more  NN 2006»
15 years 1 months ago
Machine learning approaches for estimation of prediction interval for the model output
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
Durga L. Shrestha, Dimitri P. Solomatine
PAMI
2006
107views more  PAMI 2006»
15 years 1 months ago
Robust Pose Estimation from a Planar Target
In theory, the pose of a calibrated camera can be uniquely determined from a minimum of four coplanar but noncollinear points. In practice, there are many applications of camera po...
Gerald Schweighofer, Axel Pinz
PRL
2006
85views more  PRL 2006»
15 years 1 months ago
Image segmentation based on merging of sub-optimal segmentations
In this paper a heuristic segmentation algorithm is presented based on the oversegmentation of an image. The method uses a set of different segmentations of the image produced pre...
Juan Carlos Pichel, David E. Singh, Francisco F. R...
SIGIR
2008
ACM
15 years 1 months ago
Ambiguous queries: test collections need more sense
Although there are many papers examining ambiguity in Information Retrieval, this paper shows that there is a whole class of ambiguous word that past research has barely explored....
Mark Sanderson