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ESWA
2006
103views more  ESWA 2006»
15 years 1 months ago
Model gene network by semi-fixed Bayesian network
Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, and protein or RNA transport. Thus, le...
Tie-Fei Liu, Wing-Kin Sung, Ankush Mittal
CVPR
2010
IEEE
15 years 9 months ago
Comparative object similarity for improved recognition with few or no examples
Learning models for recognizing objects with few or no training examples is important, due to the intrinsic longtailed distribution of objects in the real world. In this paper, we...
Gang Wang, David Forsyth, Derek Hoiem
CVPR
2008
IEEE
16 years 3 months ago
Rank-based distance metric learning: An application to image retrieval
We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements...
Jung-Eun Lee, Rong Jin, Anil K. Jain
ICML
1999
IEEE
16 years 2 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
JMLR
2010
106views more  JMLR 2010»
14 years 8 months ago
Why Does Unsupervised Pre-training Help Deep Learning?
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtai...
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, ...