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» Approximate dynamic programming: Lessons from the field
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NIPS
1993
13 years 9 months ago
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
Tommi Jaakkola, Michael I. Jordan, Satinder P. Sin...
CPAIOR
2008
Springer
13 years 9 months ago
Amsaa: A Multistep Anticipatory Algorithm for Online Stochastic Combinatorial Optimization
The one-step anticipatory algorithm (1s-AA) is an online algorithm making decisions under uncertainty by ignoring future non-anticipativity constraints. It makes near-optimal decis...
Luc Mercier, Pascal Van Hentenryck
CVPR
2005
IEEE
14 years 9 months ago
A Hybrid Graphical Model for Robust Feature Extraction from Video
We consider a visual scene analysis scenario where objects (e.g. people, cars) pass through the viewing field of a static camera and need to be detected and segmented from the bac...
Ali Taylan Cemgil, Wojciech Zajdel, Ben J. A. Kr&o...
ICDAR
2007
IEEE
14 years 2 months ago
Handwritten Word Recognition Using Conditional Random Fields
The paper describes a lexicon driven approach for word recognition on handwritten documents using Conditional Random Fields(CRFs). CRFs are discriminative models and do not make a...
Shravya Shetty, Harish Srinivasan, Sargur N. Sriha...
ESOP
1990
Springer
13 years 12 months ago
From Interpreting to Compiling Binding Times
The key to realistic self-applicable partial evaluation is to analyze binding times in the source program, i.e., whether the result of partially evaluating a source expression is ...
Charles Consel, Olivier Danvy