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GECCO
2009
Springer
258views Optimization» more  GECCO 2009»
15 years 11 months ago
Evolutionary learning of local descriptor operators for object recognition
Nowadays, object recognition is widely studied under the paradigm of matching local features. This work describes a genetic programming methodology that synthesizes mathematical e...
Cynthia B. Pérez, Gustavo Olague
UAI
2008
15 years 8 months ago
Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
Richard S. Sutton, Csaba Szepesvári, Alborz...
BMCBI
2010
125views more  BMCBI 2010»
15 years 6 months ago
In-silico prediction of blood-secretory human proteins using a ranking algorithm
Background: Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful inform...
Qi Liu, Juan Cui, Qiang Yang, Ying Xu
ICSE
2012
IEEE-ACM
13 years 9 months ago
Debugger Canvas: Industrial experience with the code bubbles paradigm
—At ICSE 2010, the Code Bubbles team from Brown University and the Code Canvas team from Microsoft Research presented similar ideas for new user experiences for an integrated dev...
Robert DeLine, Andrew Bragdon, Kael Rowan, Jens Ja...
ATAL
2010
Springer
15 years 7 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon