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ICML
2009
IEEE
16 years 17 days ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
16 years 4 days ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
BMCBI
2010
176views more  BMCBI 2010»
14 years 12 months ago
TargetSpy: a supervised machine learning approach for microRNA target prediction
Background: Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recen...
Martin Sturm, Michael Hackenberg, David Langenberg...
ECML
2003
Springer
15 years 5 months ago
Learning Rules to Improve a Machine Translation System
In this paper we show how to learn rules to improve the performance of a machine translation system. Given a system consisting of two translation functions (one from language A to ...
David Kauchak, Charles Elkan
105
Voted
ISMB
2000
15 years 1 months ago
A Probabilistic Learning Approach to Whole-Genome Operon Prediction
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this ...
Mark Craven, David Page, Jude W. Shavlik, Joseph B...