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» Sequential Inductive Learning
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83
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TCS
2010
14 years 11 months ago
Active learning in heteroscedastic noise
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...
András Antos, Varun Grover, Csaba Szepesv&a...
ICCV
2011
IEEE
14 years 25 days ago
Optical Flow Estimation Using Learned Sparse Model
Optical flow estimation is a fundamental and ill-posed problem in computer vision. To recover a dense flow field, appropriate spatial constraints have to be enforced. Recent ad...
Kui Jia, Xiaogang Wang, Xiaoou Tang
116
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FCCM
2005
IEEE
124views VLSI» more  FCCM 2005»
15 years 6 months ago
Parallel Hardware Implementation of Cellular Learning Automata Based Evolutionary Computing (CLA-EC) on FPGA
The CLA-EC is a model obtained by combining the concepts of cellular learning automata and evolutionary algorithms. The parallel structure of the CLA-EC makes it suitable for hard...
Arash Hariri, Reza Rastegar, Morteza Saheb Zamani,...
97
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AIEDAM
2004
96views more  AIEDAM 2004»
15 years 21 days ago
Learning while designing
: This paper reports on preliminary results of an explorative study of a protocol analysis of team learning while designing using in-situ data. Two measurement-based frameworks are...
Gourabmoy Nath, John S. Gero
138
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AIME
2005
Springer
15 years 6 months ago
Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System
We have developed an algorithm for triaging acute pediatric abdominal pain in the Emergency Department using the discovery-driven approach. This algorithm is embedded into the MET-...
Jerzy Blaszczynski, Ken Farion, Wojtek Michalowski...