63 search results - page 4 / 13 » Learning low-rank kernel matrices |
ICASSP
2011
IEEE
12 years 10 months ago
2011
IEEE
One of the main difficulties in computing information theoretic learning (ITL) estimators is the computational complexity that grows quadratically with data. Considerable amount ...
AAAI
2012
11 years 9 months ago
2012
Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we ...
TNN
2010
13 years 1 months ago
2010
Compared to Singular Value Decomposition (SVD), Generalized Low Rank Approximations of Matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yiel...
ICDM
2010
IEEE
13 years 5 months ago
2010
IEEE
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both...