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ACML
2009
Springer
15 years 9 months ago
Learning Algorithms for Domain Adaptation
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Manas A. Pathak, Eric Nyberg
CORR
2012
Springer
170views Education» more  CORR 2012»
14 years 19 days ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
ICASSP
2007
IEEE
15 years 11 months ago
Genomic Network Tomography
This paper considers the problem of learning cellular signaling networks from incomplete measurements of pathway activity. Cells respond to environmental changes (e.g., starvation...
Michael G. Rabbat, Mário A. T. Figueiredo, ...
IJBRA
2006
59views more  IJBRA 2006»
15 years 4 months ago
Predicting altered pathways using extendable scaffolds
: Many diseases, especially solid tumors, involve the disruption or deregulation of cellular processes. Most current work using gene expression and other high-throughput data, simp...
B. M. Broom, T. J. McDonnell, D. Subramanian
136
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EPIA
1995
Springer
15 years 8 months ago
Using Stochastic Grammars to Learn Robotic Tasks
Abstract. The paper introduces a reinforcement learning-based methodology for performance improvement of Intelligent Controllers. The translation interfaces of a 3-level Hierarchic...
Pedro U. Lima, George N. Saridis