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ML
2002
ACM
163views Machine Learning» more  ML 2002»
13 years 3 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola
ICASSP
2011
IEEE
12 years 7 months ago
L0 sparse graphical modeling
Graphical models are well established in providing compact conditional probability descriptions of complex multivariable interactions. In the Gaussian case, graphical models are d...
Goran Marjanovic, Victor Solo
SCALESPACE
2007
Springer
13 years 9 months ago
Non-negative Sparse Modeling of Textures
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...
Gabriel Peyré
INFOCOM
2009
IEEE
13 years 10 months ago
ALDO: An Anomaly Detection Framework for Dynamic Spectrum Access Networks
—Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that use resources in a proper, disciplined manner...
Song Liu, Yingying Chen, Wade Trappe, Larry J. Gre...
CGF
2005
252views more  CGF 2005»
13 years 3 months ago
Support Vector Machines for 3D Shape Processing
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...
Florian Steinke, Bernhard Schölkopf, Volker B...