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» Complexity Dimensions and Learnability
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PRESENCE
2000
94views more  PRESENCE 2000»
13 years 9 months ago
Virtual Environments with Four or More Spatial Dimensions
We describe methods for displaying complex, texturemapped environments with four or more spatial dimensions that allow for real-time interaction. At any one moment in time, a thre...
Michael D'Zmura, Philippe Colantoni, Gregory Seyra...
ICCV
2003
IEEE
14 years 11 months ago
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are co...
Bogdan Georgescu, Ilan Shimshoni, Peter Meer
DATE
2009
IEEE
107views Hardware» more  DATE 2009»
14 years 4 months ago
Learning early-stage platform dimensioning from late-stage timing verification
— Today's innovations in the automotive sector are, to a great extent, based on electronics. The increasing integration complexity and stringent cost reduction goals turn E/...
Kai Richter, Marek Jersak, Rolf Ernst
ICCAD
1996
IEEE
164views Hardware» more  ICCAD 1996»
14 years 1 months ago
A novel dimension reduction technique for the capacitance extraction of 3D VLSI interconnects
In this paper, we present a new capacitance extraction method named Dimension Reduction Technique (DRT) for 3D VLSI interconnects. The DRT converts a complex 3D problem into a ser...
Wei Hong II, Weikai Sun, Zhenhai Zhu, Hao Ji, Ben ...
SDM
2007
SIAM
182views Data Mining» more  SDM 2007»
13 years 11 months ago
Distance Preserving Dimension Reduction for Manifold Learning
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Hyunsoo Kim, Haesun Park, Hongyuan Zha