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BMCBI
2010
179views more  BMCBI 2010»
14 years 9 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
15 years 10 months ago
Mining concept-drifting data streams using ensemble classifiers
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
IJCAI
2007
14 years 11 months ago
Learning to Identify Unexpected Instances in the Test Set
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
Xiaoli Li, Bing Liu, See-Kiong Ng
JCB
2008
159views more  JCB 2008»
14 years 9 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther
63
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PRL
2006
129views more  PRL 2006»
14 years 9 months ago
Learning spatial relations in object recognition
This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between obj...
Thang V. Pham, Arnold W. M. Smeulders