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» Learning Functions from Imperfect Positive Data
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RECOMB
2006
Springer
14 years 9 months ago
Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data
MicroRNAs (miRNAs) have recently been discovered as an important class of non-coding RNA genes that play a major role in regulating gene expression, providing a means to control th...
Jim C. Huang, Quaid Morris, Brendan J. Frey
BMCBI
2005
151views more  BMCBI 2005»
13 years 8 months ago
stam - a Bioconductor compliant R package for structured analysis of microarray data
Background: Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles...
Claudio Lottaz, Rainer Spang
PAMI
2008
161views more  PAMI 2008»
13 years 8 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
ICCV
2003
IEEE
14 years 10 months ago
Image Statistics and Anisotropic Diffusion
Many sensing techniques and image processing applications are characterized by noisy, or corrupted, image data. Anisotropic diffusion is a popular, and theoretically well understo...
Hanno Scharr, Michael J. Black, Horst W. Haussecke...
ACL
2007
13 years 10 months ago
Bootstrapping a Stochastic Transducer for Arabic-English Transliteration Extraction
We propose a bootstrapping approach to training a memoriless stochastic transducer for the task of extracting transliterations from an English-Arabic bitext. The transducer learns...
Tarek Sherif, Grzegorz Kondrak