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SBIA
2004
Springer
15 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...
DATE
2009
IEEE
229views Hardware» more  DATE 2009»
15 years 4 months ago
Health-care electronics The market, the challenges, the progress
— Exploding health care demands and costs of aging and stressed populations necessitate the use of more in-home monitoring and personalized health care. Electronics hold great pr...
Wolfgang Eberle, Ashwin S. Mecheri, Thi Kim Thoa N...
89
Voted
ESWA
2007
176views more  ESWA 2007»
14 years 9 months ago
Credit scoring with a data mining approach based on support vector machines
The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scorin...
Cheng-Lung Huang, Mu-Chen Chen, Chieh-Jen Wang
75
Voted
NECO
2010
103views more  NECO 2010»
14 years 8 months ago
Posterior Weighted Reinforcement Learning with State Uncertainty
Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn f...
Tobias Larsen, David S. Leslie, Edmund J. Collins,...
BMCBI
2010
165views more  BMCBI 2010»
14 years 9 months ago
MTar: a computational microRNA target prediction architecture for human transcriptome
Background: MicroRNAs (miRNAs) play an essential task in gene regulatory networks by inhibiting the expression of target mRNAs. As their mRNA targets are genes involved in importa...
Vinod Chandra, Reshmi Girijadevi, Achuthsankar S. ...