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BMCBI
2010
97views more  BMCBI 2010»
15 years 27 days ago
Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach
Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on es...
Dirk Repsilber, Sabine Kern, Anna Telaar, Gerhard ...
83
Voted
ICASSP
2010
IEEE
14 years 10 months ago
Learning from other subjects helps reducing Brain-Computer Interface calibration time
A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. I...
Fabien Lotte, Cuntai Guan
ICCV
2009
IEEE
16 years 5 months ago
Learning Pedestrian Dynamics from the Real World
In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and a...
Paul Scovanner, Marshall Tappen
100
Voted
ICPR
2006
IEEE
16 years 1 months ago
A maximum margin discriminative learning algorithm for temporal signals
We propose a new maximum margin discriminative learning algorithm here for classification of temporal signals. It is superior to conventional HMM in the sense that it does not nee...
Wenjie Xu, Jiankang Wu, Zhiyong Huang
102
Voted
KDD
2004
ACM
117views Data Mining» more  KDD 2004»
16 years 1 months ago
Regularized multi--task learning
Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks...
Theodoros Evgeniou, Massimiliano Pontil