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» Approximation Algorithms for Clustering Problems
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EDBT
2004
ACM
268views Database» more  EDBT 2004»
16 years 6 months ago
DBDC: Density Based Distributed Clustering
Abstract. Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as m...
Eshref Januzaj, Hans-Peter Kriegel, Martin Pfeifle
ISPD
2005
ACM
188views Hardware» more  ISPD 2005»
15 years 12 months ago
A semi-persistent clustering technique for VLSI circuit placement
Placement is a critical component of today's physical synthesis flow with tremendous impact on the final performance of VLSI designs. However, it accounts for a significant p...
Charles J. Alpert, Andrew B. Kahng, Gi-Joon Nam, S...
TCBB
2010
166views more  TCBB 2010»
15 years 4 months ago
Approximate Maximum Parsimony and Ancestral Maximum Likelihood
— We explore the maximum parsimony (MP) and ancestral maximum likelihood (AML) criteria in phylogenetic tree reconstruction. Both problems are NP hard, so we seek approximate sol...
Noga Alon, Benny Chor, Fabio Pardi, Anat Rapoport
NIPS
2008
15 years 7 months ago
An interior-point stochastic approximation method and an L1-regularized delta rule
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
Peter Carbonetto, Mark Schmidt, Nando de Freitas
165
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LATIN
2000
Springer
15 years 10 months ago
Worst-Case Complexity of the Optimal LLL Algorithm
In this paper, we consider the open problem of the complexity of the LLL algorithm in the case when the approximation parameter of the algorithm has its extreme value
Ali Akhavi