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» Sampling Techniques for Kernel Methods
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VC
2008
169views more  VC 2008»
14 years 9 months ago
Adaptive particles for incompressible fluid simulation
Abstract We propose a particle-based technique for simulating incompressible fluid that includes adaptive refinement of particle sampling. Each particle represents a mass of fluid ...
Woosuck Hong, Donald H. House, John Keyser
TSP
2008
91views more  TSP 2008»
14 years 9 months ago
Nonlinear and Nonideal Sampling: Theory and Methods
We study a sampling setup where a continuous-time signal is mapped by a memoryless, invertible and nonlinear transformation, and then sampled in a nonideal manner. Such scenarios a...
Tsvi G. Dvorkind, Yonina C. Eldar, Ewa Matusiak
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
16 years 2 months ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
ISOLA
2010
Springer
14 years 8 months ago
LivingKnowledge: Kernel Methods for Relational Learning and Semantic Modeling
Latest results of statistical learning theory have provided techniques such us pattern analysis and relational learning, which help in modeling system behavior, e.g. the semantics ...
Alessandro Moschitti
ICIP
2001
IEEE
15 years 11 months ago
Study of embedded font context and kernel space methods for improved videotext recognition
Videotext refers to text superimposed on video frames. A videotext based Multimedia Description Scheme has recently been adopted into the MPEG-7 standard. A study of published wor...
Chitra Dorai, Hrishikesh Aradhye, Jae-Chang Shim