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SBIA
2004
Springer
14 years 2 months ago
Learning with Drift Detection
Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
João Gama, Pedro Medas, Gladys Castillo, Pe...
BMCBI
2008
99views more  BMCBI 2008»
13 years 9 months ago
Exhaustive prediction of disease susceptibility to coding base changes in the human genome
Background: Single Nucleotide Polymorphisms (SNPs) are the most abundant form of genomic variation and can cause phenotypic differences between individuals, including diseases. Ba...
Vinayak Kulkarni, Mounir Errami, Robert Barber, Ha...
JCB
2008
159views more  JCB 2008»
13 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
ICTAI
2010
IEEE
13 years 7 months ago
A Semantic Similarity Language Model to Improve Automatic Image Annotation
In recent years, with the rapid proliferation of digital images, the need to search and retrieve the images accurately, efficiently, and conveniently is becoming more acute. Automa...
Tianxia Gong, Shimiao Li, Chew Lim Tan
PROMISE
2010
13 years 3 months ago
On the value of learning from defect dense components for software defect prediction
BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...
Hongyu Zhang, Adam Nelson, Tim Menzies