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ARCS
2005
Springer
16 years 10 days ago
Adaptive Object Acquisition
We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a...
Gabriele Peters, Claus-Peter Alberts, Markus Bries...
CVPR
2011
IEEE
15 years 2 months ago
Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds
Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled ...
Sudheendra Vijayanarasimhan, Kristen Grauman
AAAI
2008
15 years 9 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
ICASSP
2011
IEEE
14 years 10 months ago
Optimal structure of memory models for lossless compression of binary image contours
In this paper we study various chain codes, which are representations of binary image contours, in terms of their ability to compress in the best way the contour information using...
Ioan Tabus, Septimia Sarbu
BMCBI
2005
116views more  BMCBI 2005»
15 years 6 months ago
Can Zipf's law be adapted to normalize microarrays?
Background: Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for ...
Timothy Lu, Christine M. Costello, Peter J. P. Cro...