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JMLR
2002
75views more  JMLR 2002»
13 years 7 months ago
Stability and Generalization
We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out e...
Olivier Bousquet, André Elisseeff
NCI
2004
141views Neural Networks» more  NCI 2004»
13 years 9 months ago
Estimating the error at given test input points for linear regression
In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand...
Masashi Sugiyama
SIAMSC
2010
192views more  SIAMSC 2010»
13 years 6 months ago
Adaptive Discontinuous Galerkin Methods for Eigenvalue Problems Arising in Incompressible Fluid Flows
In this article we consider the a posteriori error estimation and adaptive mesh refinement of discontinuous Galerkin finite element approximations of the hydrodynamic stability p...
K. Andrew Cliffe, Edward J. C. Hall, Paul Houston
TEC
2012
197views Formal Methods» more  TEC 2012»
11 years 10 months ago
Improving Generalization Performance in Co-Evolutionary Learning
Recently, the generalization framework in co-evolutionary learning has been theoretically formulated and demonstrated in the context of game-playing. Generalization performance of...
Siang Yew Chong, Peter Tino, Day Chyi Ku, Xin Yao
ADCM
2011
13 years 2 months ago
Approximation properties of the Generalized Finite Element Method
In this paper, we have obtained an approximation result in the Generalized Finite Element Method (GFEM) that reflects the global approximation property of the Partition of Unity ...
C. Anitescu, U. Banerjee