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CVPR
2010
IEEE
16 years 19 days ago
Parametric Dimensionality Reduction by Unsupervised Regression
We introduce a parametric version (pDRUR) of the recently proposed Dimensionality Reduction by Unsupervised Regression algorithm. pDRUR alternately minimizes reconstruction error ...
Miguel Carreira-perpinan, Zhengdong Lu
WADS
2007
Springer
128views Algorithms» more  WADS 2007»
15 years 11 months ago
Approximate Range Searching: The Absolute Model
Range searching is a well known problem in the area of geometric data structures. We consider this problem in the context of approximation, where an approximation parameter ε >...
Guilherme Dias da Fonseca
DAGM
2010
Springer
15 years 6 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
134
Voted
BMCBI
2008
136views more  BMCBI 2008»
15 years 5 months ago
Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models
Background: When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be incl...
Harald Binder, Martin Schumacher
COMCOM
2006
114views more  COMCOM 2006»
15 years 5 months ago
ImTCP: TCP with an inline measurement mechanism for available bandwidth
We introduce a novel mechanism for actively measuring available bandwidth along a network path. Instead of adding probe traffic to the network, the new mechanism exploits data pack...
Cao Le Thanh Man, Go Hasegawa, Masayuki Murata