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DAM
2008
111views more  DAM 2008»
15 years 6 months ago
Sums of squares based approximation algorithms for MAX-SAT
We investigate the Semidefinite Programming based Sums of squares (SOS) decomposition method, designed for global optimization of polynomials, in the context of the (Maximum) Sati...
Hans van Maaren, Linda van Norden, M. J. H. Heule
CVPR
1998
IEEE
16 years 8 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 10 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 7 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
CORR
2007
Springer
86views Education» more  CORR 2007»
15 years 6 months ago
On the Discovery of Random Trees
Abstract. We consider in this paper the problem of discovering, via a traceroute algorithm, the topology of a network, whose graph is spanned by an infinite branching process. A s...
Fabrice Guillemin, Philippe Robert