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» Set cover algorithms for very large datasets
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ICPR
2008
IEEE
15 years 4 months ago
Semi-supervised feature selection under logistic I-RELIEF framework
We consider feature selection in the semi-supervised learning setting. This problem is rarely addressed in the literature. We propose a new algorithm as a natural extension of the...
Yubo Cheng, Yunpeng Cai, Yijun Sun, Jian Li
IMR
2004
Springer
15 years 3 months ago
Entkerner: A System for Removal of Globally Invisible Triangles from Large Meshes
We present a method that computes a global potentially visible set for the complete region outside the convex hull of an object. The technique is used to remove invisible parts (t...
Manfred Ernst, Frank Firsching, Roberto Grosso
SIGGRAPH
2000
ACM
15 years 2 months ago
The digital Michelangelo project: 3D scanning of large statues
We describe a hardware and software system for digitizing the shape and color of large fragile objects under non-laboratory conditions. Our system employs laser triangulation rang...
Marc Levoy, Kari Pulli, Brian Curless, Szymon Rusi...
CHI
2011
ACM
14 years 1 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
EDBT
2012
ACM
228views Database» more  EDBT 2012»
13 years 5 days ago
Finding maximal k-edge-connected subgraphs from a large graph
In this paper, we study how to find maximal k-edge-connected subgraphs from a large graph. k-edge-connected subgraphs can be used to capture closely related vertices, and findin...
Rui Zhou, Chengfei Liu, Jeffrey Xu Yu, Weifa Liang...