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» The metamathematics of random graphs
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PODC
2011
ACM
14 years 4 months ago
MIS on trees
A maximal independent set on a graph is an inclusion-maximal set of mutually non-adjacent nodes. This basic symmetry breaking structure is vital for many distributed algorithms, w...
Christoph Lenzen, Roger Wattenhofer
CVPR
2012
IEEE
13 years 4 months ago
Submodular dictionary learning for sparse coding
A greedy-based approach to learn a compact and discriminative dictionary for sparse representation is presented. We propose an objective function consisting of two components: ent...
Zhuolin Jiang, Guangxiao Zhang, Larry S. Davis
CVPR
1998
IEEE
16 years 3 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generaliz...
Yuri Boykov, Olga Veksler, Ramin Zabih
ECML
2006
Springer
15 years 5 months ago
Multiple-Instance Learning Via Random Walk
This paper presents a decoupled two stage solution to the multiple-instance learning (MIL) problem. With a constructed affinity matrix to reflect the instance relations, a modified...
Dong Wang, Jianmin Li, Bo Zhang
AAAI
1998
15 years 3 months ago
Which Search Problems Are Random?
The typical difficulty of various NP-hard problems varies with simple parameters describing their structure. This behavior is largely independent of the search algorithm, but depe...
Tad Hogg