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» Maximum kernel density estimator for robust fitting
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DAGM
2010
Springer
13 years 8 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
NIPS
2000
13 years 8 months ago
Kernel Expansions with Unlabeled Examples
Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tra...
Martin Szummer, Tommi Jaakkola
TCBB
2008
108views more  TCBB 2008»
13 years 7 months ago
Statistical Characterization of Protein Ensembles
When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to r...
Diego Rother, Guillermo Sapiro, Vijay Pande
BMCBI
2010
175views more  BMCBI 2010»
13 years 7 months ago
Global parameter estimation methods for stochastic biochemical systems
Background: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equat...
Suresh Kumar Poovathingal, Rudiyanto Gunawan
ICML
2006
IEEE
14 years 8 months ago
Robust probabilistic projections
Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, p...
Cédric Archambeau, Michel Verleysen, Nicola...