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JMLR
2008
230views more  JMLR 2008»
13 years 7 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
BMCBI
2008
122views more  BMCBI 2008»
13 years 8 months ago
Effects of dependence in high-dimensional multiple testing problems
Background: We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) contro...
Kyung In Kim, Mark A. van de Wiel
CIBCB
2006
IEEE
14 years 1 months ago
A Stochastic model to estimate the time taken for Protein-Ligand Docking
Abstract— Quantum mechanics and molecular dynamic simulation provide important insights into structural configurations and molecular interaction data today. To extend this atomi...
Preetam Ghosh, Samik Ghosh, Kalyan Basu, Sajal K. ...
JCB
2006
215views more  JCB 2006»
13 years 7 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
CSFW
2010
IEEE
13 years 11 months ago
Approximation and Randomization for Quantitative Information-Flow Analysis
—Quantitative information-flow analysis (QIF) is an emerging technique for establishing information-theoretic confidentiality properties. Automation of QIF is an important step...
Boris Köpf, Andrey Rybalchenko