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» Learning to Classify Texts Using Positive and Unlabeled Data
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BMCBI
2008
134views more  BMCBI 2008»
13 years 7 months ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane
IEAAIE
2001
Springer
13 years 12 months ago
Selecting a Relevant Set of Examples to Learn IE-Rules
The growing availability of online text has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Some ...
Jordi Turmo, Horacio Rodríguez
JIPS
2006
72views more  JIPS 2006»
13 years 7 months ago
A Feature Selection Technique based on Distributional Differences
: This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many featur...
Sung-Dong Kim
ICIP
2008
IEEE
14 years 9 months ago
Cross-domain learning methods for high-level visual concept classification
Exploding amounts of multimedia data increasingly require automatic indexing and classification, e.g. training classifiers to produce high-level features, or semantic concepts, ch...
Wei Jiang, Eric Zavesky, Shih-Fu Chang, Alexander ...
ICML
2002
IEEE
14 years 8 months ago
Adaptive View Validation: A First Step Towards Automatic View Detection
Multi-view algorithms reduce the amount of required training data by partitioning the domain features into separate subsets or views that are sufficient to learn the target concep...
Ion Muslea, Steven Minton, Craig A. Knoblock