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JACM
2006
99views more  JACM 2006»
13 years 8 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
PR
2008
170views more  PR 2008»
13 years 7 months ago
Annealing and the normalized N-cut
We describe an annealing procedure that computes the normalized N-cut of a weighted graph G. The first phase transition computes the solution of the approximate normalized 2-cut p...
Tomás Gedeon, Albert E. Parker, Collette Ca...
KDD
2008
ACM
178views Data Mining» more  KDD 2008»
14 years 8 months ago
Training structural svms with kernels using sampled cuts
Discriminative training for structured outputs has found increasing applications in areas such as natural language processing, bioinformatics, information retrieval, and computer ...
Chun-Nam John Yu, Thorsten Joachims
FSTTCS
2009
Springer
14 years 2 months ago
Approximating Fault-Tolerant Group-Steiner Problems
In this paper, we initiate the study of designing approximation algorithms for FaultTolerant Group-Steiner (FTGS) problems. The motivation is to protect the well-studied group-Ste...
Rohit Khandekar, Guy Kortsarz, Zeev Nutov
SWAT
2004
Springer
98views Algorithms» more  SWAT 2004»
14 years 1 months ago
Tight Approximability Results for Test Set Problems in Bioinformatics
In this paper, we investigate the test set problem and its variations that appear in a variety of applications. In general, we are given a universe of objects to be “distinguish...
Piotr Berman, Bhaskar DasGupta, Ming-Yang Kao