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» Algorithms for Large, Sparse Network Alignment Problems
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BMCBI
2004
208views more  BMCBI 2004»
15 years 2 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
156
Voted
ICPP
2000
IEEE
15 years 6 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary
ICANN
2005
Springer
15 years 7 months ago
Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines
Although Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems, they suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
JVA
2006
IEEE
15 years 8 months ago
Simulated Annealing for Grid Scheduling Problem
Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographicall...
Stefka Fidanova
FOCS
2009
IEEE
15 years 9 months ago
Faster Generation of Random Spanning Trees
In this paper, we set forth a new algorithm for generating approximately uniformly random spanning trees in undirected graphs. We show how to sample from a distribution that is wi...
Jonathan A. Kelner, Aleksander Madry