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151views
14 years 5 months ago
Robust Bayesian reinforcement learning through tight lower bounds
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinfo...
Christos Dimitrakakis
CGF
2010
171views more  CGF 2010»
15 years 4 months ago
Efficient Mean-shift Clustering Using Gaussian KD-Tree
Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional ...
Chunxia Xiao, Meng Liu
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
17 years 6 days ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
KDD
2003
ACM
214views Data Mining» more  KDD 2003»
16 years 7 months ago
Adaptive duplicate detection using learnable string similarity measures
The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied...
Mikhail Bilenko, Raymond J. Mooney
KDD
2003
ACM
156views Data Mining» more  KDD 2003»
16 years 7 months ago
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal o...
Stephen D. Bay, Mark Schwabacher
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