119 search results - page 11 / 24 » Sparse kernel methods for high-dimensional survival data |
CSDA
2007
13 years 8 months ago
2007
The Lasso is an attractive regularisation method for high dimensional regression. It combines variable selection with an efficient computational procedure. However, the rate of co...
TKDE
2012
11 years 10 months ago
2012
—Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorith...
SDM
2007
SIAM
13 years 9 months ago
2007
SIAM
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
ICML
2006
IEEE
14 years 9 months ago
2006
IEEE
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to ...
TNN
2010
13 years 3 months ago
2010
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...