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» A Case Study for Learning from Imbalanced Data Sets
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BMCBI
2008
174views more  BMCBI 2008»
15 years 1 months ago
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
Cédric Auliac, Vincent Frouin, Xavier Gidro...
COLT
1992
Springer
15 years 5 months ago
Language Learning from Stochastic Input
Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the...
Shyam Kapur, Gianfranco Bilardi
JCDL
2006
ACM
119views Education» more  JCDL 2006»
15 years 7 months ago
Learning from artifacts: metadata utilization analysis
Describes the MARC Content Designation Utilization Project, which is examining a very large set of metadata records as artifacts of the library cataloging enterprise. This is the ...
William E. Moen, Shawne D. Miksa, Amy Eklund, Serh...
ISESE
2006
IEEE
15 years 7 months ago
Using observational pilot studies to test and improve lab packages
Controlled experiments are a key approach to evaluate and evolve our understanding of software engineering technologies. However, defining and running a controlled experiment is a...
Manoel G. Mendonça, Daniela Cruzes, Josemei...
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
15 years 5 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang