97 search results - page 6 / 20 » Regularized discriminant analysis for high dimensional, low ... |
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
CVPR
2008
IEEE
14 years 9 months ago
2008
IEEE
Linear Discriminant Analysis (LDA) is one of the wellknown methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope w...
BMCBI
2010
13 years 7 months ago
2010
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
CIKM
2010
Springer
13 years 4 months ago
2010
Springer
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
BMCBI
2011
12 years 11 months ago
2011
Background: Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper ...