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» Investigating practical, linear temporal difference learning
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JMLR
2006
140views more  JMLR 2006»
13 years 8 months ago
Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
Masashi Sugiyama
CONEXT
2007
ACM
13 years 10 months ago
Detecting worm variants using machine learning
Network intrusion detection systems typically detect worms by examining packet or flow logs for known signatures. Not only does this approach mean worms cannot be detected until ...
Oliver Sharma, Mark Girolami, Joseph S. Sventek
ATAL
2005
Springer
14 years 2 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
ECCV
1998
Springer
14 years 1 months ago
On Degeneracy of Linear Reconstruction from Three Views: Linear Line Complex and Applications
This paper investigates the linear degeneracies of projective structure estimation from point and line features across three views. We show that the rank of the linear system of e...
Gideon P. Stein, Amnon Shashua
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