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NEUROSCIENCE
2001
Springer
15 years 3 months ago
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
Doina Caragea, Adrian Silvescu, Vasant Honavar
124
Voted
CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
15 years 5 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
SC
2009
ACM
15 years 6 months ago
Evaluating the impact of inaccurate information in utility-based scheduling
Proponents of utility-based scheduling policies have shown the potential for a 100–1400% increase in value-delivered to users when used in lieu of traditional approaches such as...
Alvin AuYoung, Amin Vahdat, Alex C. Snoeren
JMLR
2010
172views more  JMLR 2010»
14 years 6 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
ICML
2003
IEEE
16 years 7 days ago
Learning with Knowledge from Multiple Experts
The use of domain knowledge in a learner can greatly improve the models it produces. However, high-quality expert knowledge is very difficult to obtain. Traditionally, researchers...
Matthew Richardson, Pedro Domingos