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» Terminating Decision Algorithms Optimally
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ICML
2009
IEEE
16 years 4 months ago
Learning when to stop thinking and do something!
An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we c...
Barnabás Póczos, Csaba Szepesv&aacut...
TNN
1998
76views more  TNN 1998»
15 years 2 months ago
Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: a learning-follows-d
— In this paper, we present a novel approach to partitioning pattern spaces using a multiobjective genetic algorithm for identifying (near-)optimal subspaces for hierarchical lea...
Rajeev Kumar, Peter Rockett
CVPR
2005
IEEE
16 years 5 months ago
WaldBoost - Learning for Time Constrained Sequential Detection
: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of...
Jan Sochman, Jiri Matas
ICIP
2008
IEEE
15 years 9 months ago
MAP-MRF approach for binarization of degraded document image
We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probabil...
Jung Gap Kuk, Nam Ik Cho, Kyoung Mu Lee
ICPP
1991
IEEE
15 years 6 months ago
Emulation of a PRAM on Leveled Networks
There is an interesting class of ICNs, which includes the star graph and the n-way shuffle, for which the network diameter is sub-logarithmic in the network size. This paper prese...
Michael A. Palis, Sanguthevar Rajasekaran, David S...