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ADCM
2008
136views more  ADCM 2008»
14 years 10 months ago
Learning and approximation by Gaussians on Riemannian manifolds
Learning function relations or understanding structures of data lying in manifolds embedded in huge dimensional Euclidean spaces is an important topic in learning theory. In this ...
Gui-Bo Ye, Ding-Xuan Zhou
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
15 years 1 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
AI
2010
Springer
14 years 10 months ago
Kernel functions for case-based planning
Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning p...
Ivan Serina
ICASSP
2011
IEEE
14 years 1 months ago
Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
Hanchao Qi, Shannon Hughes
ICIP
2005
IEEE
15 years 11 months ago
Hexagonal versus orthogonal lattices: a new comparison using approximation theory
We provide a new comparison between hexagonal and orthogonal lattices, based on approximation theory. For each of the lattices, we select the "natural" spline basis func...
Laurent Condat, Dimitri Van De Ville, Thierry Blu