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UAI
2004
14 years 11 days ago
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms
Bayesian learning in undirected graphical models--computing posterior distributions over parameters and predictive quantities-is exceptionally difficult. We conjecture that for ge...
Iain Murray, Zoubin Ghahramani
PAMI
2002
108views more  PAMI 2002»
13 years 10 months ago
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...
Derek C. Stanford, Adrian E. Raftery
MICCAI
2003
Springer
14 years 12 months ago
A New Brain Segmentation Framework
We present a new brain segmentation framework which we apply to T1-weighted magnetic resonance image segmentation. The innovation of the algorithm in comparison to the state-of-the...
Torsten Butz, Patric Hagmann, Eric Tardif, Reto Me...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 11 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
CVPR
2006
IEEE
15 years 1 months ago
Training Deformable Models for Localization
We present a new method for training deformable models. Assume that we have training images where part locations have been labeled. Typically, one fits a model by maximizing the l...
Deva Ramanan, Cristian Sminchisescu