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CORR
2010
Springer
188views Education» more  CORR 2010»
14 years 9 months ago
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible ...
Sahand Negahban, Pradeep Ravikumar, Martin J. Wain...
KDD
2003
ACM
150views Data Mining» more  KDD 2003»
15 years 9 months ago
Learning relational probability trees
Classification trees are widely used in the machine learning and data mining communities for modeling propositional data. Recent work has extended this basic paradigm to probabili...
Jennifer Neville, David Jensen, Lisa Friedland, Mi...
AINA
2008
IEEE
15 years 3 months ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Connie Phong, Raul Singh
NIPS
1998
14 years 10 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis
BMCBI
2005
104views more  BMCBI 2005»
14 years 9 months ago
A statistical approach for array CGH data analysis
Background: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences imm...
Franck Picard, Stéphane Robin, Marc Laviell...