90 search results - page 5 / 18 » Random Feature Subset Selection for Analysis of Data with Mi... |
IWANN
2009
Springer
14 years 3 months ago
2009
Springer
This work proposes the use of maximal variation analysis for feature selection within least squares support vector machines for survival analysis. Instead of selecting a subset of ...
JMLR
2010
13 years 3 months ago
2010
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...
UAI
2008
13 years 10 months ago
2008
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one...
ISBRA
2009
Springer
14 years 3 months ago
2009
Springer
Cheng and Church proposed a greedy deletion-addition algorithm to find a given number of k biclusters, whose mean squared residues (MSRs) are below certain thresholds and the miss...
JUCS
2008
13 years 8 months ago
2008
: Feature selection methods are often applied in the context of document classification. They are particularly important for processing large data sets that may contain millions of...