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» Generalization Bounds for Learning Kernels
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ICML
2010
IEEE
13 years 11 months ago
An Analysis of the Convergence of Graph Laplacians
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
Daniel Ting, Ling Huang, Michael I. Jordan
COLT
2000
Springer
14 years 2 months ago
Entropy Numbers of Linear Function Classes
This paper collects together a miscellany of results originally motivated by the analysis of the generalization performance of the “maximum-margin” algorithm due to Vapnik and...
Robert C. Williamson, Alex J. Smola, Bernhard Sch&...
AAAI
2008
14 years 4 days ago
Efficient Learning of Action Schemas and Web-Service Descriptions
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
Thomas J. Walsh, Michael L. Littman
COLT
2006
Springer
14 years 1 months ago
A Randomized Online Learning Algorithm for Better Variance Control
We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
Jean-Yves Audibert
ALT
2011
Springer
12 years 9 months ago
On Noise-Tolerant Learning of Sparse Parities and Related Problems
We consider the problem of learning sparse parities in the presence of noise. For learning parities on r out of n variables, we give an algorithm that runs in time poly log 1 δ , ...
Elena Grigorescu, Lev Reyzin, Santosh Vempala