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» Gene function prediction using labeled and unlabeled data
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COLING
2008
13 years 10 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
Gholamreza Haffari, Anoop Sarkar
BMCBI
2007
128views more  BMCBI 2007»
13 years 8 months ago
Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements
Background: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these ...
Hui Lan, Rachel Carson, Nicholas J. Provart, Antho...
NIPS
2003
13 years 10 months ago
Learning with Local and Global Consistency
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
ICCV
2003
IEEE
14 years 10 months ago
Minimally-Supervised Classification using Multiple Observation Sets
This paper discusses building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or...
Chris Stauffer
PKDD
2001
Springer
108views Data Mining» more  PKDD 2001»
14 years 29 days ago
Knowledge Discovery in Multi-label Phenotype Data
The biological sciences are undergoing an explosion in the amount of available data. New data analysis methods are needed to deal with the data. We present work using KDD to analys...
Amanda Clare, Ross D. King