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» Kernelization and Complexity Results for Connectivity Augmen...
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PAMI
2011
14 years 4 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
84
Voted
IPPS
2008
IEEE
15 years 4 months ago
Build to order linear algebra kernels
—The performance bottleneck for many scientific applications is the cost of memory access inside linear algebra kernels. Tuning such kernels for memory efficiency is a complex ...
Jeremy G. Siek, Ian Karlin, Elizabeth R. Jessup
ICML
2009
IEEE
15 years 10 months ago
SimpleNPKL: simple non-parametric kernel learning
Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity ...
Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
COMPGEOM
1996
ACM
15 years 1 months ago
Linear Complexity Hexahedral Mesh Generation
We show that any simply connected (but not necessarily convex) polyhedron with an even number of quadrilateral sides can be partitioned into O(n) topological cubes, meeting face t...
David Eppstein
93
Voted
TNN
2010
176views Management» more  TNN 2010»
14 years 4 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao