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IJCAI
2003
15 years 4 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
NIPS
2000
15 years 4 months ago
High-temperature Expansions for Learning Models of Nonnegative Data
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Oliver B. Downs
117
Voted
ICASSP
2010
IEEE
15 years 3 months ago
Ultrasound tomography with learned dictionaries
We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scanners. Given the measurements, our algorithm finds a...
Ivana Tosic, Ivana Jovanovic, Pascal Frossard, Mar...
162
Voted
JMLR
2006
186views more  JMLR 2006»
15 years 3 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
NN
1998
Springer
15 years 3 months ago
Distributed ARTMAP: a neural network for fast distributed supervised learning
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically req...
Gail A. Carpenter, Boriana L. Milenova, Benjamin W...