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ML
2002
ACM
154views Machine Learning» more  ML 2002»
15 years 6 months ago
Technical Update: Least-Squares Temporal Difference Learning
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
Justin A. Boyan
ICONIP
2009
15 years 4 months ago
Robust Incremental Subspace Learning for Object Tracking
In this paper, we introduce a novel incremental subspace based object tracking algorithm. The two major contributions of our work are the Robust PCA based occlusion handling scheme...
Gang Yu, Zhiwei Hu, Hongtao Lu
CVPR
2005
IEEE
16 years 8 months ago
Beyond Pairwise Clustering
We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the ...
Sameer Agarwal, Jongwoo Lim, Lihi Zelnik-Manor, Pi...
COMPGEOM
2003
ACM
16 years 2 days ago
Updating and constructing constrained delaunay and constrained regular triangulations by flips
I discuss algorithms based on bistellar flips for inserting and deleting constraining (d − 1)-facets in d-dimensional constrained Delaunay triangulations (CDTs) and weighted CD...
Jonathan Richard Shewchuk
SIAMCOMP
2010
104views more  SIAMCOMP 2010»
15 years 1 months ago
Server Scheduling to Balance Priorities, Fairness, and Average Quality of Service
Often server systems do not implement the best known algorithms for optimizing average Quality of Service (QoS) out of concern of that these algorithms may be insufficiently fair ...
Nikhil Bansal, Kirk Pruhs