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JMLR
2012
12 years 11 months ago
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical inter...
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luj...
ICML
2004
IEEE
15 years 9 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
CVPR
2006
IEEE
15 years 11 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