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» Entropy Numbers, Operators and Support Vector Kernels
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MICRO
2010
IEEE
149views Hardware» more  MICRO 2010»
13 years 5 months ago
Improving SIMT Efficiency of Global Rendering Algorithms with Architectural Support for Dynamic Micro-Kernels
Wide Single Instruction, Multiple Thread (SIMT) architectures often require a static allocation of thread groups that are executed in lockstep throughout the entire application ker...
Michael Steffen, Joseph Zambreno
TNN
2008
182views more  TNN 2008»
13 years 7 months ago
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines
Abstract--Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) ...
Ivor Wai-Hung Tsang, András Kocsor, James T...
ICCV
2011
IEEE
12 years 7 months ago
Struck: Structured Output Tracking with Kernels
Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task a...
Sam Hare, Amir Saffari, Philip H.S. Torr
ACL
2010
13 years 5 months ago
A Generalized-Zero-Preserving Method for Compact Encoding of Concept Lattices
Constructing an encoding of a concept lattice using short bit vectors allows for efficient computation of join operations on the lattice. Join is the central operation any unifica...
Matthew Skala, Victoria Krakovna, János Kra...
ICML
2005
IEEE
14 years 8 months ago
Building Sparse Large Margin Classifiers
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Bernhard Schölkopf, Gökhan H. Bakir, Min...