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» Sampling Methods for Unsupervised Learning
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136
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ISDA
2009
IEEE
15 years 9 months ago
Postponed Updates for Temporal-Difference Reinforcement Learning
This paper presents postponed updates, a new strategy for TD methods that can improve sample efficiency without incurring the computational and space requirements of model-based ...
Harm van Seijen, Shimon Whiteson
ACCV
2007
Springer
15 years 8 months ago
Combined Object Detection and Segmentation by Using Space-Time Patches
This paper presents a method for classifying the direction of movement and for segmenting objects simultaneously using features of space-time patches. Our approach uses vector quan...
Yasuhiro Murai, Hironobu Fujiyoshi, Takeo Kanade
102
Voted
EMNLP
2006
15 years 4 months ago
Better Informed Training of Latent Syntactic Features
We study unsupervised methods for learning refinements of the nonterminals in a treebank. Following Matsuzaki et al. (2005) and Prescher (2005), we may for example split NP withou...
Markus Dreyer, Jason Eisner
133
Voted
ICIC
2005
Springer
15 years 8 months ago
Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number...
Jianru Xue, Nanning Zheng, Xiaopin Zhong
UAI
2008
15 years 4 months ago
Small Sample Inference for Generalization Error in Classification Using the CUD Bound
Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
Eric Laber, Susan Murphy