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ECML
2005
Springer
14 years 1 months ago
On Discriminative Joint Density Modeling
Abstract. We study discriminative joint density models, that is, generative models for the joint density p(c, x) learned by maximizing a discriminative cost function, the condition...
Jarkko Salojärvi, Kai Puolamäki, Samuel ...
JMLR
2008
151views more  JMLR 2008»
13 years 8 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
BMCBI
2010
100views more  BMCBI 2010»
13 years 5 months ago
New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size
Background: As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several ...
Laure Sambourg, Nicolas Thierry-Mieg
ISBI
2007
IEEE
14 years 2 months ago
Multivariate Hypothesis Testing of Dti Data for Tissue Clustering
In this work we investigate the feasibility and effectiveness of unsupervised tissue clustering and classification algorithms for DTI data. Tissue clustering and classification ...
Raisa Z. Freidlin, Yaniv Assaf, Peter J. Basser
NIPS
1997
13 years 9 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung