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» Practical Preference Relations for Large Data Sets
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CVPR
2005
IEEE
14 years 10 months ago
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support ef...
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalb...
PAISI
2009
Springer
14 years 3 months ago
Discovering Compatible Top-K Theme Patterns from Text Based on Users' Preferences
Discovering a representative set of theme patterns from a large amount of text for interpreting their meaning has always been concerned by researches of both data mining and inform...
Yongxin Tong, Shilong Ma, Dan Yu, Yuanyuan Zhang, ...
BMCBI
2010
185views more  BMCBI 2010»
13 years 3 months ago
MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heu
Background: The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most compara...
Raphaël Helaers, Michel C. Milinkovitch
DLOG
2007
13 years 10 months ago
Retrospective on Clio: Schema Mapping and Data Exchange in Practice
Clio is a joint research project between the University of Toronto and IBM Almaden Research Center started in 1999 to address both foundational and systems issues related to the ma...
Renée J. Miller
ISCA
1993
IEEE
112views Hardware» more  ISCA 1993»
14 years 16 days ago
Working Sets, Cache Sizes, and Node Granularity Issues for Large-Scale Multiprocessors
The distribution of resources among processors, memory and caches is a crucial question faced by designers of large-scale parallel machines. If a machine is to solve problems with...
Edward Rothberg, Jaswinder Pal Singh, Anoop Gupta