119 search results - page 4 / 24 » Sparse kernel methods for high-dimensional survival data |
BMCBI
2008
13 years 7 months ago
2008
Background: Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles a...
IJON
2007
13 years 7 months ago
2007
In this paper, a ridgelet kernel regression model is proposed for approximation of high dimensional functions. It is based on ridgelet theory, kernel and regularization technology ...
BMCBI
2010
13 years 4 months ago
2010
Background: Most genomic data have ultra-high dimensions with more than 10,000 genes (probes). Regularization methods with L1 and Lp penalty have been extensively studied in survi...
PAMI
2010
13 years 5 months ago
2010
—Representative surface reconstruction algorithms taking a gradient field as input enforces the integrability constraint in a discrete manner. While enforcing integrability allo...
ICCS
2003
Springer
14 years 17 days ago
2003
Springer
Sparse Grids are the basis for efficient high dimensional approximation and have recently been applied successfully to predictive modelling. They are spanned by a collection of si...