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» Learning Evaluation Functions for Large Acyclic Domains
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ML
1998
ACM
136views Machine Learning» more  ML 1998»
13 years 8 months ago
Co-Evolution in the Successful Learning of Backgammon Strategy
Following Tesauro’s work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of t...
Jordan B. Pollack, Alan D. Blair
ECCV
2010
Springer
14 years 16 days ago
Detection and Tracking of Large Number of Targets in Wide Area Surveillance
In this paper, we tackle the problem of object detection and tracking in a new and challenging domain of wide area surveillance. This problem poses several challenges: large camera...
ICDM
2006
IEEE
138views Data Mining» more  ICDM 2006»
14 years 2 months ago
Adaptive Blocking: Learning to Scale Up Record Linkage
Many information integration tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as...
Mikhail Bilenko, Beena Kamath, Raymond J. Mooney
CCGRID
2007
IEEE
14 years 2 months ago
Parameter Sweeps for Functional MRI Research in the "Virtual Laboratory for e-Science" Project
Image analysis is an important component of neuroscience research. The ICT infrastructure and technical knowledge needed to perform (large scale) neuroimaging studies, however, is...
Sílvia Delgado Olabarriaga, Aart J. Nederve...
ICRA
2010
IEEE
163views Robotics» more  ICRA 2010»
13 years 7 months ago
Exploiting domain knowledge in planning for uncertain robot systems modeled as POMDPs
Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...